De-centralized load-balancing at processors

ABSTRACT

A mechanism is described for facilitating localized load-balancing for processors in computing devices. A method of embodiments, as described herein, includes facilitating hosting, at a processor of a computing device, a local load-balancing mechanism. The method may further include monitoring balancing of loads at the processor and serving as a local scheduler to maintain de-centralized load-balancing at the processor and between the processor and other one or more processors.

RELATED APPLICATION

This Application is a continuation of and claims the benefit of andpriority to U.S. application Ser. No. 16/696,848, entitledDE-CENTRALIZED LOAD-BALANCING AT PROCESSORS, by Prasoonkumar Surti, etal., filed Nov. 26, 2019, now issued as U.S. Pat. No. 10,877,815, whichis a continuation of and claims the benefit of and priority to U.S.application Ser. No. 15/477,025, entitled DE-CENTRALIZED LOAD-BALANCINGAT PROCESSORS, by Prasoonkumar Surti, et al., filed Apr. 1, 2017, nowissued as U.S. Pat. No. 10,496,448, the entire contents of which areincorporated herein by reference.

FIELD

Embodiments described herein relate generally to data processing andmore particularly to facilitate de-centralized load-balancing atprocessors.

BACKGROUND

Current parallel graphics data processing includes systems and methodsdeveloped to perform specific operations on graphics data such as, forexample, linear interpolation, tessellation, rasterization, texturemapping, depth testing, etc. Traditionally, graphics processors usedfixed function computational units to process graphics data; however,more recently, portions of graphics processors have been madeprogrammable, enabling such processors to support a wider variety ofoperations for processing vertex and fragment data.

To further increase performance, graphics processors typically implementprocessing techniques such as pipelining that attempt to process, inparallel, as much graphics data as possible throughout the differentparts of the graphics pipeline. Parallel graphics processors with singleinstruction, multiple thread (SIMT) architectures are designed tomaximize the amount of parallel processing in the graphics pipeline. Inan SIMT architecture, groups of parallel threads attempt to executeprogram instructions synchronously together as often as possible toincrease processing efficiency. A general overview of software andhardware for SIMT architectures can be found in Shane Cook, CUDAProgramming, Chapter 3, pages 37-51 (2013) and/or Nicholas Wilt. CUDAHandbook, A Comprehensive Guide to GPU Programming, Sections 2.6.2 to3.1.2 (June 2013).

Conventional techniques do not provide for uniform load-balancing acrossgraphics processor and thus, such techniques lead to performanceinefficiency.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments are illustrated by way of example, and not by way oflimitation, in the figures of the accompanying drawings in which likereference numerals refer to similar elements. So that the manner inwhich the above recited features can be understood in detail, a moreparticular description, briefly summarized above, may be had byreference to embodiments, some of which are illustrated in the appendeddrawings. It is to be noted, however, that the appended drawingsillustrate only typical embodiments and are therefore not to beconsidered limiting of its scope, for the drawings may illustrate otherequally effective embodiments.

FIG. 1 is a block diagram illustrating a computer system configured toimplement one or more aspects of the embodiments described herein.

FIG. 2A-2D illustrate a parallel processor components, according to anembodiment.

FIG. 3A-3B are block diagrams of graphics multiprocessors, according toembodiments.

FIG. 4A-4F illustrate an exemplary architecture in which a plurality ofgraphics processing units are communicatively coupled to a plurality ofmulti-core processors.

FIG. 5 is a conceptual diagram of a graphics processing pipeline,according to an embodiment.

FIG. 6 illustrates a computing device hosting a de-centralizedload-balancing scheduling mechanism according to one embodiment.

FIG. 7 illustrates a de-centralized load-balancing mechanism accordingto one embodiment.

FIG. 8 illustrates a conventional framework employing a centralizedscheduler.

FIG. 9A illustrates a novel architectural placement employing ade-centralized load-balancing mechanism according to one embodiment.

FIG. 9B illustrates a novel architectural placement employing ade-centralized load-balancing mechanism according to one embodiment.

FIG. 9C illustrates shared local memory de-fragmentation operationaccording to one embodiment.

FIG. 10 is a block diagram of an embodiment of a computer system with aprocessor having one or more processor cores and graphics processors.

FIG. 11 is a block diagram of one embodiment of a processor having oneor more processor cores, an integrated memory controller, and anintegrated graphics processor.

FIG. 12 is a block diagram of one embodiment of a graphics processorwhich may be a discreet graphics processing unit, or may be graphicsprocessor integrated with a plurality of processing cores.

FIG. 13 is a block diagram of an embodiment of a graphics processingengine for a graphics processor.

FIG. 14 is a block diagram of another embodiment of a graphicsprocessor.

FIG. 15 is a block diagram of thread execution logic including an arrayof processing elements.

FIG. 16 illustrates a graphics processor execution unit instructionformat according to an embodiment.

FIG. 17 is a block diagram of another embodiment of a graphics processorwhich includes a graphics pipeline, a media pipeline, a display engine,thread execution logic, and a render output pipeline.

FIG. 18A is a block diagram illustrating a graphics processor commandformat according to an embodiment.

FIG. 18B is a block diagram illustrating a graphics processor commandsequence according to an embodiment.

FIG. 19 illustrates exemplary graphics software architecture for a dataprocessing system according to an embodiment.

FIG. 20 is a block diagram illustrating an IP core development systemthat may be used to manufacture an integrated circuit to performoperations according to an embodiment.

FIG. 21 is a block diagram illustrating an exemplary system on a chipintegrated circuit that may be fabricated using one or more IP cores,according to an embodiment.

FIG. 22 is a block diagram illustrating an exemplary graphics processorof a system on a chip integrated circuit.

FIG. 23 is a block diagram illustrating an additional exemplary graphicsprocessor of a system on a chip integrated circuit.

DETAILED DESCRIPTION

In some embodiments, a graphics processing unit (GPU) is communicativelycoupled to host/processor cores to accelerate graphics operations,machine-learning operations, pattern analysis operations, and variousgeneral purpose GPU (GPGPU) functions. The GPU may be communicativelycoupled to the host processor/cores over a bus or another interconnect(e.g., a high-speed interconnect such as PCIe or NVLink). In otherembodiments, the GPU may be integrated on the same package or chip asthe cores and communicatively coupled to the cores over an internalprocessor bus/interconnect (i.e., internal to the package or chip).Regardless of the manner in which the GPU is connected, the processorcores may allocate work to the GPU in the form of sequences ofcommands/instructions contained in a work descriptor. The GPU then usesdedicated circuitry/logic for efficiently processing thesecommands/instructions.

In the following description, numerous specific details are set forth.However, embodiments, as described herein, may be practiced withoutthese specific details. In other instances, well-known circuits,structures and techniques have not been shown in details in order not toobscure the understanding of this description.

System Overview I

FIG. 1 is a block diagram illustrating a computing system 100 configuredto implement one or more aspects of the embodiments described herein.The computing system 100 includes a processing subsystem 101 having oneor more processor(s) 102 and a system memory 104 communicating via aninterconnection path that may include a memory hub 105. The memory hub105 may be a separate component within a chipset component or may beintegrated within the one or more processor(s) 102. The memory hub 105couples with an I/O subsystem 111 via a communication link 106. The I/Osubsystem 111 includes an I/O hub 107 that can enable the computingsystem 100 to receive input from one or more input device(s) 108.Additionally, the I/O hub 107 can enable a display controller, which maybe included in the one or more processor(s) 102, to provide outputs toone or more display device(s) 110A. In one embodiment, the one or moredisplay device(s) 110A coupled with the I/O hub 107 can include a local,internal, or embedded display device.

In one embodiment, the processing subsystem 101 includes one or moreparallel processor(s) 112 coupled to memory hub 105 via a bus or othercommunication link 113. The communication link 113 may be one of anynumber of standards based communication link technologies or protocols,such as, but not limited to PCI Express, or may be a vendor specificcommunications interface or communications fabric. In one embodiment,the one or more parallel processor(s) 112 form a computationally focusedparallel or vector processing system that an include a large number ofprocessing cores and/or processing clusters, such as a many integratedcore (MIC) processor. In one embodiment, the one or more parallelprocessor(s) 112 form a graphics processing subsystem that can outputpixels to one of the one or more display device(s) 110A coupled via theI/O Hub 107. The one or more parallel processor(s) 112 can also includea display controller and display interface (not shown) to enable adirect connection to one or more display device(s) 110B.

Within the I/O subsystem 111, a system storage unit 114 can connect tothe I/O hub 107 to provide a storage mechanism for the computing system100. An I/O switch 116 can be used to provide an interface mechanism toenable connections between the I/O hub 107 and other components, such asa network adapter 118 and/or wireless network adapter 119 that may beintegrated into the platform, and various other devices that can beadded via one or more add-in device(s) 120. The network adapter 118 canbe an Ethernet adapter or another wired network adapter. The wirelessnetwork adapter 119 can include one or more of a Wi-Fi, Bluetooth, nearfield communication (NFC), or other network device that includes one ormore wireless radios.

The computing system 100 can include other components not explicitlyshown, including USB or other port connections, optical storage drives,video capture devices, and the like, may also be connected to the I/Ohub 107. Communication paths interconnecting the various components inFIG. 1 may be implemented using any suitable protocols, such as PCI(Peripheral Component Interconnect) based protocols (e.g., PCI-Express),or any other bus or point-to-point communication interfaces and/orprotocol(s), such as the NV-Link high-speed interconnect, orinterconnect protocols known in the art.

In one embodiment, the one or more parallel processor(s) 112 incorporatecircuitry optimized for graphics and video processing, including, forexample, video output circuitry, and constitutes a graphics processingunit (GPU). In another embodiment, the one or more parallel processor(s)112 incorporate circuitry optimized for general purpose processing,while preserving the underlying computational architecture, described ingreater detail herein. In yet another embodiment, components of thecomputing system 100 may be integrated with one or more other systemelements on a single integrated circuit. For example, the one or moreparallel processor(s), 112 memory hub 105, processor(s) 102, and I/O hub107 can be integrated into a system on chip (SoC) integrated circuit.Alternatively, the components of the computing system 100 can beintegrated into a single package to form a system in package (SIP)configuration. In one embodiment, at least a portion of the componentsof the computing system 100 can be integrated into a multi-chip module(MCM), which can be interconnected with other multi-chip modules into amodular computing system.

It will be appreciated that the computing system 100 shown herein isillustrative and that variations and modifications are possible. Theconnection topology, including the number and arrangement of bridges,the number of processor(s) 102, and the number of parallel processor(s)112, may be modified as desired. For instance, in some embodiments,system memory 104 is connected to the processor(s) 102 directly ratherthan through a bridge, while other devices communicate with systemmemory 104 via the memory hub 105 and the processor(s) 102. In otheralternative topologies, the parallel processor(s) 112 are connected tothe I/O hub 107 or directly to one of the one or more processor(s) 102,rather than to the memory hub 105. In other embodiments, the I/O hub 107and memory hub 105 may be integrated into a single chip. Someembodiments may include two or more sets of processor(s) 102 attachedvia multiple sockets, which can couple with two or more instances of theparallel processor(s) 112.

Some of the particular components shown herein are optional and may notbe included in all implementations of the computing system 100. Forexample, any number of add-in cards or peripherals may be supported, orsome components may be eliminated. Furthermore, some architectures mayuse different terminology for components similar to those illustrated inFIG. 1. For example, the memory hub 105 may be referred to as aNorthbridge in some architectures, while the I/O hub 107 may be referredto as a Southbridge.

FIG. 2A illustrates a parallel processor 200, according to anembodiment. The various components of the parallel processor 200 may beimplemented using one or more integrated circuit devices, such asprogrammable processors, application specific integrated circuits(ASICs), or field programmable gate arrays (FPGA). The illustratedparallel processor 200 is a variant of the one or more parallelprocessor(s) 112 shown in FIG. 1, according to an embodiment.

In one embodiment, the parallel processor 200 includes a parallelprocessing unit 202. The parallel processing unit includes an I/O unit204 that enables communication with other devices, including otherinstances of the parallel processing unit 202. The I/O unit 204 may bedirectly connected to other devices. In one embodiment, the I/O unit 204connects with other devices via the use of a hub or switch interface,such as memory hub 105. The connections between the memory hub 105 andthe I/O unit 204 form a communication link 113. Within the parallelprocessing unit 202, the I/O unit 204 connects with a host interface 206and a memory crossbar 216, where the host interface 206 receivescommands directed to performing processing operations and the memorycrossbar 216 receives commands directed to performing memory operations.

When the host interface 206 receives a command buffer via the I/O unit204, the host interface 206 can direct work operations to perform thosecommands to a front end 208. In one embodiment, the front end 208couples with a scheduler 210, which is configured to distribute commandsor other work items to a processing cluster array 212. In oneembodiment, the scheduler 210 ensures that the processing cluster array212 is properly configured and in a valid state before tasks aredistributed to the processing clusters of the processing cluster array212.

The processing cluster array 212 can include up to “N” processingclusters (e.g., cluster 214A, cluster 214B, through cluster 214N). Eachcluster 214A-214N of the processing cluster array 212 can execute alarge number of concurrent threads. The scheduler 210 can allocate workto the clusters 214A-214N of the processing cluster array 212 usingvarious scheduling and/or work distribution algorithms, which may varydepending on the workload arising for each type of program orcomputation. The scheduling can be handled dynamically by the scheduler210, or can be assisted in part by compiler logic during compilation ofprogram logic configured for execution by the processing cluster array212.

In one embodiment, different clusters 214A-214N of processing clusterarray 212 can be allocated for processing different types of programs orfor performing different types of computations.

The processing cluster array 212 can be configured to perform varioustypes of parallel processing operations. In one embodiment, theprocessing cluster array 212 is configured to perform general-purposeparallel compute operations. For example, the processing cluster array212 can include logic to execute processing tasks including filtering ofvideo and/or audio data, performing modeling operations, includingphysics operations, and performing data transformations.

In one embodiment, the processing cluster array 212 is configured toperform parallel graphics processing operations. In embodiments in whichthe parallel processor 200 is configured to perform graphics processingoperations, the processing cluster array 212 can include additionallogic to support the execution of such graphics processing operations,including, but not limited to texture sampling logic to perform textureoperations, as well as tessellation logic and other vertex processinglogic. Additionally, the processing cluster array 212 can be configuredto execute graphics processing related shader programs such as, but notlimited to vertex shaders, tessellation shaders, geometry shaders, andpixel shaders. The parallel processing unit 202 can transfer data fromsystem memory via the I/O unit 204 for processing. During processing thetransferred data can be stored to on-chip memory (e.g., parallelprocessor memory 222) during processing, then written back to systemmemory.

In one embodiment, when the parallel processing unit 202 is used toperform graphics processing, the scheduler 210 can be configured todivide the processing workload into approximately equal sized tasks, tobetter enable distribution of the graphics processing operations tomultiple clusters 214A-214N of the processing cluster array 212. In someembodiments, portions of the processing cluster array 212 can beconfigured to perform different types of processing. For example, afirst portion may be configured to perform vertex shading and topologygeneration, a second portion may be configured to perform tessellationand geometry shading, and a third portion may be configured to performpixel shading or other screen space operations, to produce a renderedimage for display. Intermediate data produced by one or more of theclusters 214A-214N may be stored in buffers to allow the intermediatedata to be transmitted between clusters 214A-214N for furtherprocessing.

During operation, the processing cluster array 212 can receiveprocessing tasks to be executed via the scheduler 210, which receivescommands defining processing tasks from front end 208. For graphicsprocessing operations, processing tasks can include indices of data tobe processed, e.g., surface (patch) data, primitive data, vertex data,and/or pixel data, as well as state parameters and commands defining howthe data is to be processed (e.g., what program is to be executed). Thescheduler 210 may be configured to fetch the indices corresponding tothe tasks or may receive the indices from the front end 208. The frontend 208 can be configured to ensure the processing cluster array 212 isconfigured to a valid state before the workload specified by incomingcommand buffers (e.g., batch-buffers, push buffers, etc.) is initiated.

Each of the one or more instances of the parallel processing unit 202can couple with parallel processor memory 222. The parallel processormemory 222 can be accessed via the memory crossbar 216, which canreceive memory requests from the processing cluster array 212 as well asthe I/O unit 204. The memory crossbar 216 can access the parallelprocessor memory 222 via a memory interface 218. The memory interface218 can include multiple partition units (e.g., partition unit 220A,partition unit 220B, through partition unit 220N) that can each coupleto a portion (e.g., memory unit) of parallel processor memory 222. Inone implementation, the number of partition units 220A-220N isconfigured to be equal to the number of memory units, such that a firstpartition unit 220A has a corresponding first memory unit 224A, a secondpartition unit 220B has a corresponding memory unit 224B, and an Nthpartition unit 220N has a corresponding Nth memory unit 224N. In otherembodiments, the number of partition units 220A-220N may not be equal tothe number of memory devices.

In various embodiments, the memory units 224A-224N can include varioustypes of memory devices, including dynamic random access memory (DRAM)or graphics random access memory, such as synchronous graphics randomaccess memory (SGRAM), including graphics double data rate (GDDR)memory. In one embodiment, the memory units 224A-224N may also include3D stacked memory, including but not limited to high bandwidth memory(HBM). Persons skilled in the art will appreciate that the specificimplementation of the memory units 224A-224N can vary, and can beselected from one of various conventional designs. Render targets, suchas frame buffers or texture maps may be stored across the memory units224A-224N, allowing partition units 220A-220N to write portions of eachrender target in parallel to efficiently use the available bandwidth ofparallel processor memory 222. In some embodiments, a local instance ofthe parallel processor memory 222 may be excluded in favor of a unifiedmemory design that utilizes system memory in conjunction with localcache memory.

In one embodiment, any one of the clusters 214A-214N of the processingcluster array 212 can process data that will be written to any of thememory units 224A-224N within parallel processor memory 222. The memorycrossbar 216 can be configured to transfer the output of each cluster214A-214N to any partition unit 220A-220N or to another cluster214A-214N, which can perform additional processing operations on theoutput. Each cluster 214A-214N can communicate with the memory interface218 through the memory crossbar 216 to read from or write to variousexternal memory devices. In one embodiment, the memory crossbar 216 hasa connection to the memory interface 218 to communicate with the I/Ounit 204, as well as a connection to a local instance of the parallelprocessor memory 222, enabling the processing units within the differentprocessing clusters 214A-214N to communicate with system memory or othermemory that is not local to the parallel processing unit 202. In oneembodiment, the memory crossbar 216 can use virtual channels to separatetraffic streams between the clusters 214A-214N and the partition units220A-220N.

While a single instance of the parallel processing unit 202 isillustrated within the parallel processor 200, any number of instancesof the parallel processing unit 202 can be included. For example,multiple instances of the parallel processing unit 202 can be providedon a single add-in card, or multiple add-in cards can be interconnected.The different instances of the parallel processing unit 202 can beconfigured to inter-operate even if the different instances havedifferent numbers of processing cores, different amounts of localparallel processor memory, and/or other configuration differences. Forexample, and in one embodiment, some instances of the parallelprocessing unit 202 can include higher precision floating point unitsrelative to other instances. Systems incorporating one or more instancesof the parallel processing unit 202 or the parallel processor 200 can beimplemented in a variety of configurations and form factors, includingbut not limited to desktop, laptop, or handheld personal computers,servers, workstations, game consoles, and/or embedded systems.

FIG. 2B is a block diagram of a partition unit 220, according to anembodiment. In one embodiment, the partition unit 220 is an instance ofone of the partition units 220A-220N of FIG. 2A. As illustrated, thepartition unit 220 includes an L2 cache 221, a frame buffer interface225, and a ROP 226 (raster operations unit). The L2 cache 221 is aread/write cache that is configured to perform load and store operationsreceived from the memory crossbar 216 and ROP 226. Read misses andurgent write-back requests are output by L2 cache 221 to frame bufferinterface 225 for processing. Dirty updates can also be sent to theframe buffer via the frame buffer interface 225 for opportunisticprocessing. In one embodiment, the frame buffer interface 225 interfaceswith one of the memory units in parallel processor memory, such as thememory units 224A-224N of FIG. 2A (e.g., within parallel processormemory 222).

In graphics applications, the ROP 226 is a processing unit that performsraster operations, such as stencil, z test, blending, and the like. TheROP 226 then outputs processed graphics data that is stored in graphicsmemory. In some embodiments, the ROP 226 includes compression logic tocompress z or color data that is written to memory and decompress z orcolor data that is read from memory. In some embodiments, the ROP 226 isincluded within each processing cluster (e.g., cluster 214A-214N of FIG.2A) instead of within the partition unit 220. In such embodiment, readand write requests for pixel data are transmitted over the memorycrossbar 216 instead of pixel fragment data.

The processed graphics data may be displayed on a display device, suchas one of the one or more display device(s) 110 of FIG. 1, routed forfurther processing by the processor(s) 102, or routed for furtherprocessing by one of the processing entities within the parallelprocessor 200 of FIG. 2A.

FIG. 2C is a block diagram of a processing cluster 214 within a parallelprocessing unit, according to an embodiment. In one embodiment, theprocessing cluster is an instance of one of the processing clusters214A-214N of FIG. 2A. The processing cluster 214 can be configured toexecute many threads in parallel, where the term “thread” refers to aninstance of a particular program executing on a particular set of inputdata. In some embodiments, single-instruction, multiple-data (SIMD)instruction issue techniques are used to support parallel execution of alarge number of threads without providing multiple independentinstruction units. In other embodiments, single-instruction,multiple-thread (SIMT) techniques are used to support parallel executionof a large number of generally synchronized threads, using a commoninstruction unit configured to issue instructions to a set of processingengines within each one of the processing clusters. Unlike a SIMDexecution regime, where all processing engines typically executeidentical instructions, SIMT execution allows different threads to morereadily follow divergent execution paths through a given thread program.Persons skilled in the art will understand that a SIMD processing regimerepresents a functional subset of a SIMT processing regime.

Operation of the processing cluster 214 can be controlled via a pipelinemanager 232 that distributes processing tasks to SIMT parallelprocessors. The pipeline manager 232 receives instructions from thescheduler 210 of FIG. 2A and manages execution of those instructions viaa graphics multiprocessor 234 and/or a texture unit 236. The illustratedgraphics multiprocessor 234 is an exemplary instance of an SIMT parallelprocessor. However, various types of SIMT parallel processors ofdiffering architectures may be included within the processing cluster214. One or more instances of the graphics multiprocessor 234 can beincluded within a processing cluster 214. The graphics multiprocessor234 can process data and a data crossbar 240 can be used to distributethe processed data to one of multiple possible destinations, includingother shader units. The pipeline manager 232 can facilitate thedistribution of processed data by specifying destinations for processeddata to be distributed vis the data crossbar 240.

Each graphics multiprocessor 234 within the processing cluster 214 caninclude an identical set of functional execution logic (e.g., arithmeticlogic units, load-store units, etc.). The functional execution logic canbe configured in a pipelined manner in which new instructions can beissued before previous instructions are complete. The functionalexecution logic may be provided. The functional logic supports a varietyof operations including integer and floating point arithmetic comparisonoperations, Boolean operations bit-shifting, and computation of variousalgebraic functions. In one embodiment, the same functional-unithardware can be leveraged to perform different operations and anycombination of functional units may be present.

The instructions transmitted to the processing cluster 214 constitutes athread. A set of threads executing across the set of parallel processingengines is a thread group. A thread group executes the same program ondifferent input data. Each thread within a thread group can be assignedto a different processing engine within a graphics multiprocessor 234. Athread group may include fewer threads than the number of processingengines within the graphics multiprocessor 234. When a thread groupincludes fewer threads than the number of processing engines, one ormore of the processing engines may be idle during cycles in which thatthread group is being processed. A thread group may also include morethreads than the number of processing engines within the graphicsmultiprocessor 234. When the thread group includes more threads than thenumber of processing engines within the graphics multiprocessor 234,processing can be performed over consecutive clock cycles. In oneembodiment, multiple thread groups can be executed concurrently on agraphics multiprocessor 234.

In one embodiment, the graphics multiprocessor 234 includes an internalcache memory to perform load and store operations. In one embodiment,the graphics multiprocessor 234 can forego an internal cache and use acache memory (e.g., L1 cache 308) within the processing cluster 214.Each graphics multiprocessor 234 also has access to L2 caches within thepartition units (e.g., partition units 220A-220N of FIG. 2A) that areshared among all processing clusters 214 and may be used to transferdata between threads. The graphics multiprocessor 234 may also accessoff-chip global memory, which can include one or more of local parallelprocessor memory and/or system memory. Any memory external to theparallel processing unit 202 may be used as global memory. Embodimentsin which the processing cluster 214 includes multiple instances of thegraphics multiprocessor 234 can share common instructions and data,which may be stored in the L1 cache 308.

Each processing cluster 214 may include an MMU 245 (memory managementunit) that is configured to map virtual addresses into physicaladdresses. In other embodiments, one or more instances of the MMU 245may reside within the memory interface 218 of FIG. 2A. The MMU 245includes a set of page table entries (PTEs) used to map a virtualaddress to a physical address of a tile (talk more about tiling) andoptionally a cache line index. The MMU 245 may include addresstranslation lookaside buffers (TLB) or caches that may reside within thegraphics multiprocessor 234 or the L1 cache or processing cluster 214.The physical address is processed to distribute surface data accesslocality to allow efficient request interleaving among partition units.The cache line index may be used to determine whether a request for acache line is a hit or miss.

In graphics and computing applications, a processing cluster 214 may beconfigured such that each graphics multiprocessor 234 is coupled to atexture unit 236 for performing texture mapping operations, e.g.,determining texture sample positions, reading texture data, andfiltering the texture data. Texture data is read from an internaltexture L1 cache (not shown) or in some embodiments from the L1 cachewithin graphics multiprocessor 234 and is fetched from an L2 cache,local parallel processor memory, or system memory, as needed. Eachgraphics multiprocessor 234 outputs processed tasks to the data crossbar240 to provide the processed task to another processing cluster 214 forfurther processing or to store the processed task in an L2 cache, localparallel processor memory, or system memory via the memory crossbar 216.A preROP 242 (pre-raster operations unit) is configured to receive datafrom graphics multiprocessor 234, direct data to ROP units, which may belocated with partition units as described herein (e.g., partition units220A-220N of FIG. 2A). The preROP 242 unit can perform optimizations forcolor blending, organize pixel color data, and perform addresstranslations.

It will be appreciated that the core architecture described herein isillustrative and that variations and modifications are possible. Anynumber of processing units, e.g., graphics multiprocessor 234, textureunits 236, preROPs 242, etc., may be included within a processingcluster 214. Further, while only one processing cluster 214 is shown, aparallel processing unit as described herein may include any number ofinstances of the processing cluster 214. In one embodiment, eachprocessing cluster 214 can be configured to operate independently ofother processing clusters 214 using separate and distinct processingunits, L1 caches, etc.

FIG. 2D shows a graphics multiprocessor 234, according to oneembodiment. In such embodiment, the graphics multiprocessor 234 coupleswith the pipeline manager 232 of the processing cluster 214. Thegraphics multiprocessor 234 has an execution pipeline including but notlimited to an instruction cache 252, an instruction unit 254, an addressmapping unit 256, a register file 258, one or more general purposegraphics processing unit (GPGPU) cores 262, and one or more load/storeunits 266. The GPGPU cores 262 and load/store units 266 are coupled withcache memory 272 and shared memory 270 via a memory and cacheinterconnect 268.

In one embodiment, the instruction cache 252 receives a stream ofinstructions to execute from the pipeline manager 232. The instructionsare cached in the instruction cache 252 and dispatched for execution bythe instruction unit 254. The instruction unit 254 can dispatchinstructions as thread groups (e.g., warps), with each thread of thethread group assigned to a different execution unit within GPGPU core262. An instruction can access any of a local, shared, or global addressspace by specifying an address within a unified address space. Theaddress mapping unit 256 can be used to translate addresses in theunified address space into a distinct memory address that can beaccessed by the load/store units 266.

The register file 258 provides a set of registers for the functionalunits of the graphics multiprocessor 324. The register file 258 providestemporary storage for operands connected to the data paths of thefunctional units (e.g., GPGPU cores 262, load/store units 266) of thegraphics multiprocessor 324. In one embodiment, the register file 258 isdivided between each of the functional units such that each functionalunit is allocated a dedicated portion of the register file 258. In oneembodiment, the register file 258 is divided between the different warpsbeing executed by the graphics multiprocessor 324.

The GPGPU cores 262 can each include floating point units (FPUs) and/orinteger arithmetic logic units (ALUs) that are used to executeinstructions of the graphics multiprocessor 324. The GPGPU cores 262 canbe similar in architecture or can differ in architecture, according toembodiments. For example, and in one embodiment, a first portion of theGPGPU cores 262 include a single precision FPU and an integer ALU whilea second portion of the GPGPU cores include a double precision FPU. Inone embodiment, the FPUs can implement the IEEE 754-2008 standard forfloating point arithmetic or enable variable precision floating pointarithmetic. The graphics multiprocessor 324 can additionally include oneor more fixed function or special function units to perform specificfunctions such as copy rectangle or pixel blending operations. In oneembodiment one or more of the GPGPU cores can also include fixed orspecial function logic.

The memory and cache interconnect 268 is an interconnect network thatconnects each of the functional units of the graphics multiprocessor 324to the register file 258 and to the shared memory 270. In oneembodiment, the memory and cache interconnect 268 is a crossbarinterconnect that allows the load/store unit 266 to implement load andstore operations between the shared memory 270 and the register file258. The register file 258 can operate at the same frequency as theGPGPU cores 262, thus data transfer between the GPGPU cores 262 and theregister file 258 is very low latency. The shared memory 270 can be usedto enable communication between threads that execute on the functionalunits within the graphics multiprocessor 234. The cache memory 272 canbe used as a data cache for example, to cache texture data communicatedbetween the functional units and the texture unit 236. The shared memory270 can also be used as a program managed cached. Threads executing onthe GPGPU cores 262 can programmatically store data within the sharedmemory in addition to the automatically cached data that is storedwithin the cache memory 272.

FIGS. 3A-3B illustrate additional graphics multiprocessors, according toembodiments. The illustrated graphics multiprocessors 325, 350 arevariants of the graphics multiprocessor 234 of FIG. 2C. The illustratedgraphics multiprocessors 325, 350 can be configured as a streamingmultiprocessor (SM) capable of simultaneous execution of a large numberof execution threads.

FIG. 3A shows a graphics multiprocessor 325 according to an additionalembodiment. The graphics multiprocessor 325 includes multiple additionalinstances of execution resource units relative to the graphicsmultiprocessor 234 of FIG. 2D. For example, the graphics multiprocessor325 can include multiple instances of the instruction unit 332A-332B,register file 334A-334B, and texture unit(s) 344A-344B. The graphicsmultiprocessor 325 also includes multiple sets of graphics or computeexecution units (e.g., GPGPU core 336A-336B, GPGPU core 337A-337B, GPGPUcore 338A-338B) and multiple sets of load/store units 340A-340B. In oneembodiment, the execution resource units have a common instruction cache330, texture and/or data cache memory 342, and shared memory 346. Thevarious components can communicate via an interconnect fabric 327. Inone embodiment, the interconnect fabric 327 includes one or morecrossbar switches to enable communication between the various componentsof the graphics multiprocessor 325.

FIG. 3B shows a graphics multiprocessor 350 according to an additionalembodiment. The graphics processor includes multiple sets of executionresources 356A-356D, where each set of execution resource includesmultiple instruction units, register files, GPGPU cores, and load storeunits, as illustrated in FIG. 2D and FIG. 3A. The execution resources356A-356D can work in concert with texture unit(s) 360A-360D for textureoperations, while sharing an instruction cache 354, and shared memory362. In one embodiment, the execution resources 356A-356D can share aninstruction cache 354 and shared memory 362, as well as multipleinstances of a texture and/or data cache memory 358A-358B. The variouscomponents can communicate via an interconnect fabric 352 similar to theinterconnect fabric 327 of FIG. 3A.

Persons skilled in the art will understand that the architecturedescribed in FIGS. 1, 2A-2D, and 3A-3B are descriptive and not limitingas to the scope of the present embodiments. Thus, the techniquesdescribed herein may be implemented on any properly configuredprocessing unit, including, without limitation, one or more mobileapplication processors, one or more desktop or server central processingunits (CPUs) including multi-core CPUs, one or more parallel processingunits, such as the parallel processing unit 202 of FIG. 2A, as well asone or more graphics processors or special purpose processing units,without departure from the scope of the embodiments described herein.

In some embodiments, a parallel processor or GPGPU as described hereinis communicatively coupled to host/processor cores to accelerategraphics operations, machine-learning operations, pattern analysisoperations, and various general purpose GPU (GPGPU) functions. The GPUmay be communicatively coupled to the host processor/cores over a bus orother interconnect (e.g., a high-speed interconnect such as PCIe orNVLink). In other embodiments, the GPU may be integrated on the samepackage or chip as the cores and communicatively coupled to the coresover an internal processor bus/interconnect (i.e., internal to thepackage or chip). Regardless of the manner in which the GPU isconnected, the processor cores may allocate work to the GPU in the formof sequences of commands/instructions contained in a work descriptor.The GPU then uses dedicated circuitry/logic for efficiently processingthese commands/instructions.

Techniques for GPU to Host Processor Interconnection

FIG. 4A illustrates an exemplary architecture in which a plurality ofGPUs 410-413 are communicatively coupled to a plurality of multi-coreprocessors 405-406 over high-speed links 440-443 (e.g., buses,point-to-point interconnects, etc.). In one embodiment, the high-speedlinks 440-443 support a communication throughput of 4 GB/s, 30 GB/s, 80GB/s or higher, depending on the implementation. Various interconnectprotocols may be used including, but not limited to, PCIe 4.0 or 5.0 andNVLink 2.0. However, the underlying principles of the invention are notlimited to any particular communication protocol or throughput.

In addition, in one embodiment, two or more of the GPUs 410-413 areinterconnected over high-speed links 444-445, which may be implementedusing the same or different protocols/links than those used forhigh-speed links 440-443. Similarly, two or more of the multi-coreprocessors 405-406 may be connected over high speed link 433 which maybe symmetric multi-processor (SMP) buses operating at 20 GB/s, 30 GB/s,120 GB/s or higher. Alternatively, all communication between the varioussystem components shown in FIG. 4A may be accomplished using the sameprotocols/links (e.g., over a common interconnection fabric). Asmentioned, however, the underlying principles of the invention are notlimited to any particular type of interconnect technology.

In one embodiment, each multi-core processor 405-406 is communicativelycoupled to a processor memory 401-402, via memory interconnects 430-431,respectively, and each GPU 410-413 is communicatively coupled to GPUmemory 420-423 over GPU memory interconnects 450-453, respectively. Thememory interconnects 430-431 and 450-453 may utilize the same ordifferent memory access technologies. By way of example, and notlimitation, the processor memories 401-402 and GPU memories 420-423 maybe volatile memories such as dynamic random access memories (DRAMs)(including stacked DRAMs), Graphics DDR SDRAM (GDDR) (e.g., GDDR5,GDDR6), or High Bandwidth Memory (HBM) and/or may be non-volatilememories such as 3D XPoint or Nano-Ram. In one embodiment, some portionof the memories may be volatile memory and another portion may benon-volatile memory (e.g., using a two-level memory (2LM) hierarchy).

As described below, although the various processors 405-406 and GPUs410-413 may be physically coupled to a particular memory 401-402,420-423, respectively, a unified memory architecture may be implementedin which the same virtual system address space (also referred to as the“effective address” space) is distributed among all of the variousphysical memories. For example, processor memories 401-402 may eachcomprise 64 GB of the system memory address space and GPU memories420-423 may each comprise 32 GB of the system memory address space(resulting in a total of 256 GB addressable memory in this example).

FIG. 4B illustrates additional details for an interconnection between amulti-core processor 407 and a graphics acceleration module 446 inaccordance with one embodiment. The graphics acceleration module 446 mayinclude one or more GPU chips integrated on a line card which is coupledto the processor 407 via the high-speed link 440. Alternatively, thegraphics acceleration module 446 may be integrated on the same packageor chip as the processor 407.

The illustrated processor 407 includes a plurality of cores 460A-460D,each with a translation lookaside buffer 461A-461D and one or morecaches 462A-462D. The cores may include various other components forexecuting instructions and processing data which are not illustrated toavoid obscuring the underlying principles of the invention (e.g.,instruction fetch units, branch prediction units, decoders, executionunits, reorder buffers, etc.). The caches 462A-462D may comprise level 1(L1) and level 2 (L2) caches. In addition, one or more shared caches 426may be included in the caching hierarchy and shared by sets of the cores460A-460D. For example, one embodiment of the processor 407 includes 24cores, each with its own L1 cache, twelve shared L2 caches, and twelveshared L3 caches. In this embodiment, one of the L2 and L3 caches areshared by two adjacent cores. The processor 407 and the graphicsaccelerator integration module 446 connect with system memory 441, whichmay include processor memories 401-402.

Coherency is maintained for data and instructions stored in the variouscaches 462A-462D, 456 and system memory 441 via inter-core communicationover a coherence bus 464. For example, each cache may have cachecoherency logic/circuitry associated therewith to communicate to overthe coherence bus 464 in response to detected reads or writes toparticular cache lines. In one implementation, a cache snooping protocolis implemented over the coherence bus 464 to snoop cache accesses. Cachesnooping/coherency techniques are well understood by those of skill inthe art and will not be described in detail here to avoid obscuring theunderlying principles of the invention.

In one embodiment, a proxy circuit 425 communicatively couples thegraphics acceleration module 446 to the coherence bus 464, allowing thegraphics acceleration module 446 to participate in the cache coherenceprotocol as a peer of the cores. In particular, an interface 435provides connectivity to the proxy circuit 425 over high-speed link 440(e.g., a PCIe bus, NVLink, etc.) and an interface 437 connects thegraphics acceleration module 446 to the link 440.

In one implementation, an accelerator integration circuit 436 providescache management, memory access, context management, and interruptmanagement services on behalf of a plurality of graphics processingengines 431, 432, N of the graphics acceleration module 446. Thegraphics processing engines 431, 432, N may each comprise a separategraphics processing unit (GPU). Alternatively, the graphics processingengines 431, 432, N may comprise different types of graphics processingengines within a GPU such as graphics execution units, media processingengines (e.g., video encoders/decoders), samplers, and blit engines. Inother words, the graphics acceleration module may be a GPU with aplurality of graphics processing engines 431-432, N or the graphicsprocessing engines 431-432, N may be individual GPUs integrated on acommon package, line card, or chip.

In one embodiment, the accelerator integration circuit 436 includes amemory management unit (MMU) 439 for performing various memorymanagement functions such as virtual-to-physical memory translations(also referred to as effective-to-real memory translations) and memoryaccess protocols for accessing system memory 441. The MMU 439 may alsoinclude a translation lookaside buffer (TLB) (not shown) for caching thevirtual/effective to physical/real address translations. In oneimplementation, a cache 438 stores commands and data for efficientaccess by the graphics processing engines 431-432, N. In one embodiment,the data stored in cache 438 and graphics memories 433-434, N is keptcoherent with the core caches 462A-462D, 456 and system memory 411. Asmentioned, this may be accomplished via proxy circuit 425 which takespart in the cache coherency mechanism on behalf of cache 438 andmemories 433-434, N (e.g., sending updates to the cache 438 related tomodifications/accesses of cache lines on processor caches 462A-462D, 456and receiving updates from the cache 438).

A set of registers 445 store context data for threads executed by thegraphics processing engines 431-432, N and a context management circuit448 manages the thread contexts. For example, the context managementcircuit 448 may perform save and restore operations to save and restorecontexts of the various threads during contexts switches (e.g., where afirst thread is saved and a second thread is stored so that the secondthread can be execute by a graphics processing engine). For example, ona context switch, the context management circuit 448 may store currentregister values to a designated region in memory (e.g., identified by acontext pointer). It may then restore the register values when returningto the context. In one embodiment, an interrupt management circuit 447receives and processes interrupts received from system devices.

In one implementation, virtual/effective addresses from a graphicsprocessing engine 431 are translated to real/physical addresses insystem memory 411 by the MMU 439. One embodiment of the acceleratorintegration circuit 436 supports multiple (e.g., 4, 8, 16) graphicsaccelerator modules 446 and/or other accelerator devices. The graphicsaccelerator module 446 may be dedicated to a single application executedon the processor 407 or may be shared between multiple applications. Inone embodiment, a virtualized graphics execution environment ispresented in which the resources of the graphics processing engines431-432, N are shared with multiple applications or virtual machines(VMs). The resources may be subdivided into “slices” which are allocatedto different VMs and/or applications based on the processingrequirements and priorities associated with the VMs and/or applications.

Thus, the accelerator integration circuit acts as a bridge to the systemfor the graphics acceleration module 446 and provides addresstranslation and system memory cache services. In addition, theaccelerator integration circuit 436 may provide virtualizationfacilities for the host processor to manage virtualization of thegraphics processing engines, interrupts, and memory management.

Because hardware resources of the graphics processing engines 431-432, Nare mapped explicitly to the real address space seen by the hostprocessor 407, any host processor can address these resources directlyusing an effective address value. One function of the acceleratorintegration circuit 436, in one embodiment, is the physical separationof the graphics processing engines 431-432, N so that they appear to thesystem as independent units.

As mentioned, in the illustrated embodiment, one or more graphicsmemories 433-434, M are coupled to each of the graphics processingengines 431-432, N, respectively. The graphics memories 433-434, M storeinstructions and data being processed by each of the graphics processingengines 431-432, N. The graphics memories 433-434, M may be volatilememories such as DRAMs (including stacked DRAMs), GDDR memory (e.g.,GDDR5, GDDR6), or HBM, and/or may be non-volatile memories such as 3DXPoint or Nano-Ram.

In one embodiment, to reduce data traffic over link 440, biasingtechniques are used to ensure that the data stored in graphics memories433-434, M is data which will be used most frequently by the graphicsprocessing engines 431-432, N and preferably not used by the cores460A-460D (at least not frequently). Similarly, the biasing mechanismattempts to keep data needed by the cores (and preferably not thegraphics processing engines 431-432, N) within the caches 462A-462D, 456of the cores and system memory 411.

FIG. 4C illustrates another embodiment in which the acceleratorintegration circuit 436 is integrated within the processor 407. In thisembodiment, the graphics processing engines 431-432, N communicatedirectly over the high-speed link 440 to the accelerator integrationcircuit 436 via interface 437 and interface 435 (which, again, may beutilize any form of bus or interface protocol). The acceleratorintegration circuit 436 may perform the same operations as thosedescribed with respect to FIG. 4B, but potentially at a higherthroughput given its close proximity to the coherency bus 462 and caches462A-462D, 426.

One embodiment supports different programming models including adedicated-process programming model (no graphics acceleration modulevirtualization) and shared programming models (with virtualization). Thelatter may include programming models which are controlled by theaccelerator integration circuit 436 and programming models which arecontrolled by the graphics acceleration module 446.

In one embodiment of the dedicated process model, graphics processingengines 431-432, N are dedicated to a single application or processunder a single operating system. The single application can funnel otherapplication requests to the graphics engines 431-432, N, providingvirtualization within a VM/partition.

In the dedicated-process programming models, the graphics processingengines 431-432, N, may be shared by multiple VM/application partitions.The shared models require a system hypervisor to virtualize the graphicsprocessing engines 431-432, N to allow access by each operating system.For single-partition systems without a hypervisor, the graphicsprocessing engines 431-432, N are owned by the operating system. In bothcases, the operating system can virtualize the graphics processingengines 431-432, N to provide access to each process or application.

For the shared programming model, the graphics acceleration module 446or an individual graphics processing engine 431-432, N selects a processelement using a process handle. In one embodiment, process elements arestored in system memory 411 and are addressable using the effectiveaddress to real address translation techniques described herein. Theprocess handle may be an implementation-specific value provided to thehost process when registering its context with the graphics processingengine 431-432, N (that is, calling system software to add the processelement to the process element linked list). The lower 16-bits of theprocess handle may be the offset of the process element within theprocess element linked list.

FIG. 4D illustrates an exemplary accelerator integration slice 490. Asused herein, a “slice” comprises a specified portion of the processingresources of the accelerator integration circuit 436. Applicationeffective address space 482 within system memory 411 stores processelements 483. In one embodiment, the process elements 483 are stored inresponse to GPU invocations 481 from applications 480 executed on theprocessor 407. A process element 483 contains the process state for thecorresponding application 480. A work descriptor (WD) 484 contained inthe process element 483 can be a single job requested by an applicationor may contain a pointer to a queue of jobs. In the latter case, the WD484 is a pointer to the job request queue in the application's addressspace 482.

The graphics acceleration module 446 and/or the individual graphicsprocessing engines 431-432, N can be shared by all or a subset of theprocesses in the system. Embodiments of the invention include aninfrastructure for setting up the process state and sending a WD 484 toa graphics acceleration module 446 to start a job in a virtualizedenvironment.

In one implementation, the dedicated-process programming model isimplementation-specific. In this model, a single process owns thegraphics acceleration module 446 or an individual graphics processingengine 431. Because the graphics acceleration module 446 is owned by asingle process, the hypervisor initializes the accelerator integrationcircuit 436 for the owning partition and the operating systeminitializes the accelerator integration circuit 436 for the owningprocess at the time when the graphics acceleration module 446 isassigned.

In operation, a WD fetch unit 491 in the accelerator integration slice490 fetches the next WD 484 which includes an indication of the work tobe done by one of the graphics processing engines of the graphicsacceleration module 446. Data from the WD 484 may be stored in registers445 and used by the MMU 439, interrupt management circuit 447 and/orcontext management circuit 446 as illustrated. For example, oneembodiment of the MMU 439 includes segment/page walk circuitry foraccessing segment/page tables 486 within the OS virtual address space485. The interrupt management circuit 447 may process interrupt events492 received from the graphics acceleration module 446. When performinggraphics operations, an effective address 493 generated by a graphicsprocessing engine 431-432, N is translated to a real address by the MMU439.

In one embodiment, the same set of registers 445 are duplicated for eachgraphics processing engine 431-432, N and/or graphics accelerationmodule 446 and may be initialized by the hypervisor or operating system.Each of these duplicated registers may be included in an acceleratorintegration slice 490. Exemplary registers that may be initialized bythe hypervisor are shown in Table 1.

TABLE 1 Hypervisor Initialized Registers 1 Slice Control Register 2 RealAddress (RA) Scheduled Processes Area Pointer 3 Authority Mask OverrideRegister 4 Interrupt Vector Table Entry Offset 5 Interrupt Vector TableEntry Limit 6 State Register 7 Logical Partition ID 8 Real address (RA)Hypervisor Accelerator Utilization Record Pointer 9 Storage DescriptionRegister

Exemplary registers that may be initialized by the operating system areshown in Table 2.

TABLE 2 Operating System Initialized Registers 1 Process and ThreadIdentification 2 Effective Address (EA) Context Save/Restore Pointer 3Virtual Address (VA) Accelerator Utilization Record Pointer 4 VirtualAddress (VA) Storage Segment Table Pointer 5 Authority Mask 6 Workdescriptor

In one embodiment, each WD 484 is specific to a particular graphicsacceleration module 446 and/or graphics processing engines 431-432, N.It contains all the information a graphics processing engine 431-432, Nrequires to do its work or it can be a pointer to a memory locationwhere the application has set up a command queue of work to becompleted.

FIG. 4E illustrates additional details for one embodiment of a sharedmodel. This embodiment includes a hypervisor real address space 498 inwhich a process element list 499 is stored. The hypervisor real addressspace 498 is accessible via a hypervisor 496 which virtualizes thegraphics acceleration module engines for the operating system 495.

The shared programming models allow for all or a subset of processesfrom all or a subset of partitions in the system to use a graphicsacceleration module 446. There are two programming models where thegraphics acceleration module 446 is shared by multiple processes andpartitions: time-sliced shared and graphics directed shared.

In this model, the system hypervisor 496 owns the graphics accelerationmodule 446 and makes its function available to all operating systems495. For a graphics acceleration module 446 to support virtualization bythe system hypervisor 496, the graphics acceleration module 446 mayadhere to the following requirements: 1) An application's job requestmust be autonomous (that is, the state does not need to be maintainedbetween jobs), or the graphics acceleration module 446 must provide acontext save and restore mechanism. 2) An application's job request isguaranteed by the graphics acceleration module 446 to complete in aspecified amount of time, including any translation faults, or thegraphics acceleration module 446 provides the ability to preempt theprocessing of the job. 3) The graphics acceleration module 446 must beguaranteed fairness between processes when operating in the directedshared programming model.

In one embodiment, for the shared model, the application 480 is requiredto make an operating system 495 system call with a graphics accelerationmodule 446 type, a work descriptor (WD), an authority mask register(AMR) value, and a context save/restore area pointer (CSRP). Thegraphics acceleration module 446 type describes the targetedacceleration function for the system call. The graphics accelerationmodule 446 type may be a system-specific value. The WD is formattedspecifically for the graphics acceleration module 446 and can be in theform of a graphics acceleration module 446 command, an effective addresspointer to a user-defined structure, an effective address pointer to aqueue of commands, or any other data structure to describe the work tobe done by the graphics acceleration module 446. In one embodiment, theAMR value is the AMR state to use for the current process. The valuepassed to the operating system is similar to an application setting theAMR. If the accelerator integration circuit 436 and graphicsacceleration module 446 implementations do not support a User AuthorityMask Override Register (UAMOR), the operating system may apply thecurrent UAMOR value to the AMR value before passing the AMR in thehypervisor call. The hypervisor 496 may optionally apply the currentAuthority Mask Override Register (AMOR) value before placing the AMRinto the process element 483. In one embodiment, the CSRP is one of theregisters 445 containing the effective address of an area in theapplication's address space 482 for the graphics acceleration module 446to save and restore the context state. This pointer is optional if nostate is required to be saved between jobs or when a job is preempted.The context save/restore area may be pinned system memory.

Upon receiving the system call, the operating system 495 may verify thatthe application 480 has registered and been given the authority to usethe graphics acceleration module 446. The operating system 495 thencalls the hypervisor 496 with the information shown in Table 3.

TABLE 3 OS to Hypervisor Call Parameters 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)

Upon receiving the hypervisor call, the hypervisor 496 verifies that theoperating system 495 has registered and been given the authority to usethe graphics acceleration module 446. The hypervisor 496 then puts theprocess element 483 into the process element linked list for thecorresponding graphics acceleration module 446 type. The process elementmay include the information shown in Table 4

TABLE 4 Process Element Information 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)8 Interrupt vector table, derived from the hypervisor call parameters. 9A state register (SR) value 10 A logical partition ID (LPID) 11 A realaddress (RA) hypervisor accelerator utilization record pointer 12 TheStorage Descriptor Register (SDR)

In one embodiment, the hypervisor initializes a plurality of acceleratorintegration slice 490 registers 445.

As illustrated in FIG. 4F, one embodiment of the invention employs aunified memory addressable via a common virtual memory address spaceused to access the physical processor memories 401-402 and GPU memories420-423. In this implementation, operations executed on the GPUs 410-413utilize the same virtual/effective memory address space to access theprocessors memories 401-402 and vice versa, thereby simplifyingprogrammability. In one embodiment, a first portion of thevirtual/effective address space is allocated to the processor memory401, a second portion to the second processor memory 402, a thirdportion to the GPU memory 420, and so on. The entire virtual/effectivememory space (sometimes referred to as the effective address space) isthereby distributed across each of the processor memories 401-402 andGPU memories 420-423, allowing any processor or GPU to access anyphysical memory with a virtual address mapped to that memory.

In one embodiment, bias/coherence management circuitry 494A-494E withinone or more of the MMUs 439A-439E ensures cache coherence between thecaches of the host processors (e.g., 405) and the GPUs 410-413 andimplements biasing techniques indicating the physical memories in whichcertain types of data should be stored. While multiple instances ofbias/coherence management circuitry 494A-494E are illustrated in FIG.4F, the bias/coherence circuitry may be implemented within the MMU ofone or more host processors 405 and/or within the acceleratorintegration circuit 436.

One embodiment allows GPU-attached memory 420-423 to be mapped as partof system memory, and accessed using shared virtual memory (SVM)technology, but without suffering the typical performance drawbacksassociated with full system cache coherence. The ability to GPU-attachedmemory 420-423 to be accessed as system memory without onerous cachecoherence overhead provides a beneficial operating environment for GPUoffload. This arrangement allows the host processor 405 software tosetup operands and access computation results, without the overhead oftradition I/O DMA data copies. Such traditional copies involve drivercalls, interrupts and memory mapped I/O (MMIO) accesses that are allinefficient relative to simple memory accesses. At the same time, theability to access GPU attached memory 420-423 without cache coherenceoverheads can be critical to the execution time of an offloadedcomputation. In cases with substantial streaming write memory traffic,for example, cache coherence overhead can significantly reduce theeffective write bandwidth seen by a GPU 410-413. The efficiency ofoperand setup, the efficiency of results access, and the efficiency ofGPU computation all play a role in determining the effectiveness of GPUoffload.

In one implementation, the selection of between GPU bias and hostprocessor bias is driven by a bias tracker data structure. A bias tablemay be used, for example, which may be a page-granular structure (i.e.,controlled at the granularity of a memory page) that includes 1 or 2bits per GPU-attached memory page. The bias table may be implemented ina stolen memory range of one or more GPU-attached memories 420-423, withor without a bias cache in the GPU 410-413 (e.g., to cachefrequently/recently used entries of the bias table). Alternatively, theentire bias table may be maintained within the GPU.

In one implementation, the bias table entry associated with each accessto the GPU-attached memory 420-423 is accessed prior the actual accessto the GPU memory, causing the following operations. First, localrequests from the GPU 410-413 that find their page in GPU bias areforwarded directly to a corresponding GPU memory 420-423. Local requestsfrom the GPU that find their page in host bias are forwarded to theprocessor 405 (e.g., over a high-speed link as discussed above). In oneembodiment, requests from the processor 405 that find the requested pagein host processor bias complete the request like a normal memory read.Alternatively, requests directed to a GPU-biased page may be forwardedto the GPU 410-413. The GPU may then transition the page to a hostprocessor bias if it is not currently using the page.

The bias state of a page can be changed either by a software-basedmechanism, a hardware-assisted software-based mechanism, or, for alimited set of cases, a purely hardware-based mechanism.

One mechanism for changing the bias state employs an API call (e.g.OpenCL), which, in turn, calls the GPU's device driver which, in turn,sends a message (or enqueues a command descriptor) to the GPU directingit to change the bias state and, for some transitions, perform a cacheflushing operation in the host. The cache flushing operation is requiredfor a transition from host processor 405 bias to GPU bias, but is notrequired for the opposite transition.

In one embodiment, cache coherency is maintained by temporarilyrendering GPU-biased pages uncacheable by the host processor 405. Toaccess these pages, the processor 405 may request access from the GPU410 which may or may not grant access right away, depending on theimplementation. Thus, to reduce communication between the processor 405and GPU 410 it is beneficial to ensure that GPU-biased pages are thosewhich are required by the GPU but not the host processor 405 and viceversa.

Graphics Processing Pipeline

FIG. 5 illustrates a graphics processing pipeline 500, according to anembodiment. In one embodiment, a graphics processor can implement theillustrated graphics processing pipeline 500. The graphics processor canbe included within the parallel processing subsystems as describedherein, such as the parallel processor 200 of FIG. 2A, which, in oneembodiment, is a variant of the parallel processor(s) 112 of FIG. 1. Thevarious parallel processing systems can implement the graphicsprocessing pipeline 500 via one or more instances of the parallelprocessing unit (e.g., parallel processing unit 202 of FIG. 2A) asdescribed herein. For example, a shader unit (e.g., graphicsmultiprocessor 234 of FIG. 2D) may be configured to perform thefunctions of one or more of a vertex processing unit 504, a tessellationcontrol processing unit 508, a tessellation evaluation processing unit512, a geometry processing unit 516, and a fragment/pixel processingunit 524. The functions of data assembler 502, primitive assemblers 506,514, 518, tessellation unit 510, rasterizer 522, and raster operationsunit 526 may also be performed by other processing engines within aprocessing cluster (e.g., processing cluster 214 of FIG. 3A) and acorresponding partition unit (e.g., partition unit 220A-220N of FIG.2C). The graphics processing pipeline 500 may also be implemented usingdedicated processing units for one or more functions. In one embodiment,one or more portions of the graphics processing pipeline 500 can beperformed by parallel processing logic within a general-purposeprocessor (e.g., CPU). In one embodiment, one or more portions of thegraphics processing pipeline 500 can access on-chip memory (e.g.,parallel processor memory 222 as in FIG. 2A) via a memory interface 528,which may be an instance of the memory interface 218 of FIG. 2A.

In one embodiment, the data assembler 502 is a processing unit thatcollects vertex data for surfaces and primitives. The data assembler 502then outputs the vertex data, including the vertex attributes, to thevertex processing unit 504. The vertex processing unit 504 is aprogrammable execution unit that executes vertex shader programs,lighting and transforming vertex data as specified by the vertex shaderprograms. The vertex processing unit 504 reads data that is stored incache, local or system memory for use in processing the vertex data andmay be programmed to transform the vertex data from an object-basedcoordinate representation to a world space coordinate space or anormalized device coordinates space.

A first instance of a primitive assembler 506 receives vertex attributesfrom the vertex processing unit 504. The primitive assembler 506readings stored vertex attributes as needed and constructs graphicsprimitives for processing by tessellation control processing unit 508.The graphics primitives include triangles, line segments, points,patches, and so forth, as supported by various graphics processingapplication programming interfaces (APIs).

The tessellation control processing unit 508 treats the input verticesas control points for a geometric patch. The control points aretransformed from an input representation from the patch (e.g., thepatch's bases) to a representation that is suitable for use in surfaceevaluation by the tessellation evaluation processing unit 512. Thetessellation control processing unit 508 can also compute tessellationfactors for edges of geometric patches. A tessellation factor applies toa single edge and quantifies a view-dependent level of detail associatedwith the edge. A tessellation unit 510 is configured to receive thetessellation factors for edges of a patch and to tessellate the patchinto multiple geometric primitives such as line, triangle, orquadrilateral primitives, which are transmitted to a tessellationevaluation processing unit 512. The tessellation evaluation processingunit 512 operates on parameterized coordinates of the subdivided patchto generate a surface representation and vertex attributes for eachvertex associated with the geometric primitives.

A second instance of a primitive assembler 514 receives vertexattributes from the tessellation evaluation processing unit 512, readingstored vertex attributes as needed, and constructs graphics primitivesfor processing by the geometry processing unit 516. The geometryprocessing unit 516 is a programmable execution unit that executesgeometry shader programs to transform graphics primitives received fromprimitive assembler 514 as specified by the geometry shader programs. Inone embodiment, the geometry processing unit 516 is programmed tosubdivide the graphics primitives into one or more new graphicsprimitives and calculate parameters used to rasterize the new graphicsprimitives.

In some embodiments, the geometry processing unit 516 can add or deleteelements in the geometry stream. The geometry processing unit 516outputs the parameters and vertices specifying new graphics primitivesto primitive assembler 518. The primitive assembler 518 receives theparameters and vertices from the geometry processing unit 516 andconstructs graphics primitives for processing by a viewport scale, cull,and clip unit 520. The geometry processing unit 516 reads data that isstored in parallel processor memory or system memory for use inprocessing the geometry data. The viewport scale, cull, and clip unit520 performs clipping, culling, and viewport scaling and outputsprocessed graphics primitives to a rasterizer 522.

The rasterizer 522 can perform depth culling and other depth-basedoptimizations. The rasterizer 522 also performs scan conversion on thenew graphics primitives to generate fragments and outputs thosefragments and associated coverage data to the fragment/pixel processingunit 524.

The fragment/pixel processing unit 524 is a programmable execution unitthat is configured to execute fragment shader programs or pixel shaderprograms. The fragment/pixel processing unit 524 transforming fragmentsor pixels received from rasterizer 522, as specified by the fragment orpixel shader programs. For example, the fragment/pixel processing unit524 may be programmed to perform operations included but not limited totexture mapping, shading, blending, texture correction and perspectivecorrection to produce shaded fragments or pixels that are output to araster operations unit 526. The fragment/pixel processing unit 524 canread data that is stored in either the parallel processor memory or thesystem memory for use when processing the fragment data. Fragment orpixel shader programs may be configured to shade at sample, pixel, tile,or other granularities, depending on the sampling rate configured forthe processing units.

The raster operations unit 526 is a processing unit that performs rasteroperations including, but not limited to stencil, z test, blending, andthe like, and outputs pixel data as processed graphics data to bestorage in graphics memory, e.g., parallel processor memory 222 as inFIG. 2A, and/or system memory 104 as in FIG. 1, to be displayed on theone or more display device(s) 110 or for further processing by one ofthe one or more processor(s) 102 or parallel processor(s) 112. In someembodiments, the raster operations unit 526 is configured to compress zor color data that is written to memory and decompress z or color datathat is read from memory.

FIG. 6 illustrates a computing device 600 hosting a de-centralizedload-balancing mechanism (“de-centralized mechanism”) 610 according toone embodiment. Computing device 600 represents a communication and dataprocessing device including (but not limited to) smart wearable devices,smartphones, virtual reality (VR) devices, head-mounted display (HMDs),mobile computers, Internet of Things (IoT) devices, laptop computers,desktop computers, server computers, etc., and be similar to or the sameas computing device 100 of FIG. 1; accordingly, for brevity, clarity,and ease of understanding, many of the details stated above withreference to FIGS. 1-5 are not further discussed or repeated hereafter.

Computing device 600 may further include (without limitations) anautonomous machine or an artificially intelligent agent, such as amechanical agent or machine, an electronics agent or machine, a virtualagent or machine, an electro-mechanical agent or machine, etc. Examplesof autonomous machines or artificially intelligent agents may include(without limitation) robots, autonomous vehicles (e.g., self-drivingcars, self-flying planes, self-sailing boats, etc.), autonomousequipment (self-operating construction vehicles, self-operating medicalequipment, etc.), and/or the like. Throughout this document, “computingdevice” may be interchangeably referred to as “autonomous machine” or“artificially intelligent agent” or simply “robot”.

Computing device 600 may further include (without limitations) largecomputing systems, such as server computers, desktop computers, etc.,and may further include set-top boxes (e.g., Internet-based cabletelevision set-top boxes, etc.), global positioning system (GPS)-baseddevices, etc. Computing device 600 may include mobile computing devicesserving as communication devices, such as cellular phones includingsmartphones, personal digital assistants (PDAs), tablet computers,laptop computers, e-readers, smart televisions, television platforms,wearable devices (e.g., glasses, watches, bracelets, smartcards,jewelry, clothing items, etc.), media players, etc. For example, in oneembodiment, computing device 600 may include a mobile computing deviceemploying a computer platform hosting an integrated circuit (“IC”), suchas system on a chip (“SoC” or “SOC”), integrating various hardwareand/or software components of computing device 600 on a single chip.

As illustrated, in one embodiment, computing device 600 may include anynumber and type of hardware and/or software components, such as (withoutlimitation) graphics processing unit (“GPU” or simply “graphicsprocessor”) 614, graphics driver (also referred to as “GPU driver”,“graphics driver logic”, “driver logic”, user-mode driver (UMD), UMD,user-mode driver framework (UMDF), UMDF, or simply “driver”) 616,central processing unit (“CPU” or simply “application processor”) 612,memory 608, network devices, drivers, or the like, as well asinput/output (I/O) sources 604, such as touchscreens, touch panels,touch pads, virtual or regular keyboards, virtual or regular mice,ports, connectors, etc. Computing device 600 may include operatingsystem (OS) 606 serving as an interface between hardware and/or physicalresources of the computer device 600 and a user. It is contemplated thatgraphics processor 614 and application processor 612 may be one or moreof processor(s) 102 of FIG. 1 and/or processor(s) 1002 of FIG. 10.

It is to be appreciated that a lesser or more equipped system than theexample described above may be preferred for certain implementations.Therefore, the configuration of computing device 600 may vary fromimplementation to implementation depending upon numerous factors, suchas price constraints, performance requirements, technologicalimprovements, or other circumstances.

Embodiments may be implemented as any or a combination of: one or moremicrochips or integrated circuits interconnected using a parentboard,hardwired logic, software stored by a memory device and executed by amicroprocessor, firmware, an application specific integrated circuit(ASIC), and/or a field programmable gate array (FPGA). The terms“logic”, “module”, “component”, “engine”, and “mechanism” may include,by way of example, software or hardware and/or combinations of softwareand hardware.

In one embodiment, de-centralized mechanism 610 may be hosted orfacilitated by operating system 606 of computing device 600. In anotherembodiment, de-centralized mechanism 610 may be hosted by or part ofgraphics processing unit (“GPU” or simply “graphics processor”) 614 orfirmware of graphics processor 614. Similarly, in yet anotherembodiment, de-centralized mechanism 610 may be hosted by or part of amicrocontroller. In yet another embodiment, de-centralized mechanism 610may be hosted by or part of any number and type of components ofcomputing device 600, such as a portion of de-centralized mechanism 610may be hosted by or part of operating system 606, another portion may behosted by or part of graphics processor 614, another portion may behosted by or part of a microcontroller, while one or more portions ofde-centralized mechanism 610 may be hosted by or part of any number andtype of other devices of computing device 600. It is contemplated thatone or more portions or components of de-centralized mechanism 610 maybe employed as hardware, software, and/or firmware.

In the illustrated embodiment, de-centralized mechanism 610 is shown asbeing hosted by or part of graphics processor 614 but that embodimentsare not limited as such. It is contemplated that embodiments are notlimited to any particular implementation or hosting of de-centralizedmechanism 610 and that de-centralized mechanism 610 and one or more ofits components may be implemented as hardware, software, firmware, orany combination thereof.

Computing device 600 may host network interface(s) to provide access toa network, such as a LAN, a wide area network (WAN), a metropolitan areanetwork (MAN), a personal area network (PAN), Bluetooth, a cloudnetwork, a mobile network (e.g., 3^(rd) Generation (3G), 4^(th)Generation (4G), etc.), an intranet, the Internet, etc. Networkinterface(s) may include, for example, a wireless network interfacehaving antenna, which may represent one or more antenna(e). Networkinterface(s) may also include, for example, a wired network interface tocommunicate with remote devices via network cable, which may be, forexample, an Ethernet cable, a coaxial cable, a fiber optic cable, aserial cable, or a parallel cable.

Embodiments may be provided, for example, as a computer program productwhich may include one or more machine-readable media having storedthereon machine-executable instructions that, when executed by one ormore machines such as a computer, network of computers, or otherelectronic devices, may result in the one or more machines carrying outoperations in accordance with embodiments described herein. Amachine-readable medium may include, but is not limited to, floppydiskettes, optical disks, CD-ROMs (Compact Disc-Read Only Memories), andmagneto-optical disks, ROMs, RAMs, EPROMs (Erasable Programmable ReadOnly Memories), EEPROMs (Electrically Erasable Programmable Read OnlyMemories), magnetic or optical cards, flash memory, or other type ofmedia/machine-readable medium suitable for storing machine-executableinstructions.

Moreover, embodiments may be downloaded as a computer program product,wherein the program may be transferred from a remote computer (e.g., aserver) to a requesting computer (e.g., a client) by way of one or moredata signals embodied in and/or modulated by a carrier wave or otherpropagation medium via a communication link (e.g., a modem and/ornetwork connection).

Throughout the document, term “user” may be interchangeably referred toas “viewer”, “observer”, “person”, “individual”, “end-user”, and/or thelike. It is to be noted that throughout this document, terms like“graphics domain” may be referenced interchangeably with “graphicsprocessing unit”, “graphics processor”, or simply “GPU” and similarly,“CPU domain” or “host domain” may be referenced interchangeably with“computer processing unit”, “application processor”, or simply “CPU”.

It is to be noted that terms like “node”, “computing node”, “server”,“server device”, “cloud computer”, “cloud server”, “cloud servercomputer”, “machine”, “host machine”, “device”, “computing device”,“computer”, “computing system”, and the like, may be usedinterchangeably throughout this document. It is to be further noted thatterms like “application”, “software application”, “program”, “softwareprogram”, “package”, “software package”, and the like, may be usedinterchangeably throughout this document. Also, terms like “job”,“input”, “request”, “message”, and the like, may be used interchangeablythroughout this document.

FIG. 7 illustrates de-centralized mechanism 610 of FIG. 6 according toone embodiment. For brevity, many of the details already discussed withreference to FIGS. 1-6 are not repeated or discussed hereafter. In oneembodiment, de-centralized mechanism 610 may include any number and typeof components, such as (without limitations): detection/migration logic701; copy engine reference logic 703; scheduling/loading logic 705; andcommunication/compatibility logic 707.

As aforementioned, a global resource, such as a parser, may becomebottlenecked in distributing tasks smaller than a runlist level todistribute multiple slices or SMs or SMMs to keep them occupied to getthe best performance for GPGPU tasks. Typically, for those dispatchesthat are based on thread groups, several thread groups from multipletask queues or contexts can have different shader complexity. Thisresults in load-balancing issues which cannot be addressed by using theconventional centralized scheduler. Once dispatch to a slice or SM,conventional techniques do not provide for load balance dynamically, anylater.

Embodiments provide for a novel technique, as facilitated byde-centralized mechanism 610, to allow for threads or thread groups tomove or migrate from one slice or SM or SMM or even graphics processor614 to another slice or SM or SMM (throughout this document, “slice”,“SM”, and “SMM” are referenced interchangeably or, for brevity, simplyas “slice” or “slice/SM/SMM”) or graphics processor, respectively, usingcopy engines both dynamically and without involving the conventionalcentralized scheduler.

In one embodiment, as illustrated with reference to FIG. 9,de-centralized mechanism 610 being hosted by graphics processor 614 canbe used as a local scheduling system, where each slice of graphicsprocessor 614 (and/or graphics processor 614 itself) is capable ofmonitoring compute utilization relating to execution units (EUs), suchas running threads, scheduled threads, etc., as facilitated bydetection/migration logic 701. Further, usingcommunication/compatibility logic 707, this monitored or detectedutilization may be communicated to neighboring slices and/or graphicsprocessors using a communication link, such as using a simple protocolon an existing physical link. This protocol may then be furtherfacilitated by communication/compatibility logic 707 to periodically orcontinuously or on-demand communicate with neighboring slices and/orgraphics processors regarding the utilization and thus, based on thetopology of connection, each slice and/or graphics processor 614 gets aview of its neighbor's utilization.

As further facilitated by detection/migration logic 701, each slice iscapable of evaluating its neighbors and own load and accordingly,decides whether to move or migrate any of the threads, thread groups,and/or tasks (for brevity, collectively referred to as “threads”) to theneighboring threads, thread groups, and/or tasks, respectively, byattempting to reserve the empty thread slots. Since there might be asense of race or rush of threads to multiple neighbors, a tie-breakermay be employed and used in cases when multiple threads are tied for thespot, such that a time may be broken based on the age of the threadswanting to migrate or the task-level execution priority set by thecommand sequencer at a global level or, in some embodiments, theapplication-level priority as determined by the operating system, suchas operating system 606 of FIG. 6.

Once a reservation succeeds, based on the above criteria andtie-breaker, copy engine may be enabled to start copying the threadcontext as facilitated by a copy engine reference logic 703. Forexample, any necessary state and data for threads (such as those thatare yet to start or paused or stalled) are mapped using a memory mappedaddress space, such as src and dest address spaces are known and a fixedset of data is needed to be migrated depending on the stalled versus yetto launch threads. For example, using copy engine reference logic 703, acopy engine may at the src of the link may gather address-space for srcand start streaming data to the destination address space, where thisallows for a rapid movement of any thread state context.

In one embodiment, a final synchronization of completion of the threadsis communicated on to the originating slice and/or graphics processor614 as facilitated by communication/compatibility logic 707. By allowingthis migration of threads among neighbors, power and latencies havetighter upper bounds and constant overhead for the most part, as opposedto routing to the farthest slice and/or graphics processor that is leastloaded as determined and executed by scheduling/loading logic 705. Inone embodiment, once the necessary monitoring and migrations are made bydetection/migration logic 701 and copy engine tasks are performed ascopy engine reference logic 703 and communications are facilitated bycommunication/compatibility logic 707, scheduling/loading logic 705 maythen serve as a local scheduler to ensure the application of thecriteria and execution of load balancing that is fast, accurate,efficient, and local.

Moreover, conventional thread group scheduling techniques use analgorithm that tries to load-balance hardware resources at scheduletime; however, as some threads or thread groups finish early, the systemtypically goes out-of-balance over time. This issue often leads tofragmentation of shared local memory (SLM) space and hardware barriers,which can prevent scheduling of new thread groups if none of a slice hassufficient SLM space due to fragmentation.

Embodiments further provide for a novel technique, as facilitated byde-centralized mechanism 610, for dynamic load-balancing of dualsub-slice (DSS) or SMs/SMMs, where any of the threads or thread groupsmay be migrated from one slice to another and so on and/or the SLM spaceof a running thread group may be moved from one location to another(such as within the same slice) to achieve better load-balancing andde-fragmentation of SLM.

In one embodiment, detection/migration logic 701 of de-centralizedmechanism 610 may periodically monitor or check the load-balancing andSLM space fragmentation of all slices of graphics processor 614. If, forexample, detection/migration logic 701 detects that load-balancing isunbalanced or out-of-balance, such as due to some threads or threadgroups being completed, detection/migration logic 701 may then migratethose threads or thread groups from one slice another slice.

Subsequently, in one embodiment, scheduling/loading logic 705 mayscheduling a new thread or thread group to take over ifdetection/migration logic 701 detects that it may not schedule thethread/thread group due to fragmentation of the SLM, such as due tothere being no single slice with sufficient SLM space to take over. Inthis case, detection/migration logic 701 may migrate the thread/threadgroups from one slice to another slice to effectively de-fragment theSLM space and create enough free space so that a new thread/thread groupmay be scheduled to take over. This novel technique allows for ensuringbalancing of load (such as graphics processor load) across all hardwareresources more uniformly, while yielding better performance efficiency.

Communication/compatibility logic 707 may be used to facilitate dynamiccommunication and compatibility between computing device 600 and anynumber and type of other computing devices (such as mobile computingdevice, desktop computer, server computing device, etc.); processingdevices or components (such as CPUs, GPUs, etc.);capturing/sensing/detecting devices (such as capturing/sensingcomponents including cameras, depth sensing cameras, camera sensors, redgreen blue (“RGB” or “rgb”) sensors, microphones, etc.); display devices(such as output components including display screens, display areas,display projectors, etc.); user/context-awareness components and/oridentification/verification sensors/devices (such as biometricsensors/detectors, scanners, etc.); database(s) 730, such as memory orstorage devices, databases, and/or data sources (such as data storagedevices, hard drives, solid-state drives, hard disks, memory cards ordevices, memory circuits, etc.); communication medium(s) 725, such asone or more communication channels or networks (e.g., cloud networks,the Internet, intranets, cellular networks, proximity networks, such asBluetooth, Bluetooth low energy (BLE), Bluetooth Smart, Wi-Fi proximity,Radio Frequency Identification (RFID), Near Field Communication (NFC),Body Area Network (BAN), etc.); wireless or wired communications andrelevant protocols (e.g., Wi-Fi®, WiMAX, Ethernet, etc.); connectivityand location management techniques; software applications/websites(e.g., social and/or business networking websites, etc., businessapplications, games and other entertainment applications, etc.); andprogramming languages, etc., while ensuring compatibility with changingtechnologies, parameters, protocols, standards, etc.

Throughout this document, terms like “logic”, “component”, “module”,“framework”, “engine”, “mechanism”, and the like, may be referencedinterchangeably and include, by way of example, software, hardware,and/or any combination of software and hardware, such as firmware. Inone example, “logic” may refer to or include a software component thatis capable of working with one or more of an operating system (e.g.,operating system 606), a graphics driver (e.g., graphics driver 616),etc., of a computing device, such as computing device 600. In anotherexample, “logic” may refer to or include a hardware component that iscapable of being physically installed along with or as part of one ormore system hardware elements, such as an application processor (e.g.,CPU 612), a graphics processor (e.g., GPU 614), etc., of a computingdevice, such as computing device 600. In yet another embodiment, “logic”may refer to or include a firmware component that is capable of beingpart of system firmware, such as firmware of an application processor(e.g., CPU 612) or a graphics processor (e.g., GPU 614), etc., of acomputing device, such as computing device 600.

Further, any use of a particular brand, word, term, phrase, name, and/oracronym, such as “GPU”, “GPU domain”, “GPGPU”, “CPU”, “CPU domain”,“graphics driver”, “workload”, “application”, “graphics pipeline”,“pipeline processes”, “slice”, “sub-slice”, “dual subs-slice”, “DSS”,“streaming multiprocessor”, “SM”, “SMM”, “load-balancing”, “migrating”,“copy engine”, “shared local memory”, “SLM”, “virtual machine”, “virtualfunction”, “API”, “3D API”, “OpenGL®”, “DirectX®”, “hardware”,“software”, “agent”, “graphics driver”, “kernel mode graphics driver”,“user-mode driver”, “user-mode driver framework”, “buffer”, “graphicsbuffer”, “task”, “process”, “operation”, “software application”, “game”,etc., should not be read to limit embodiments to software or devicesthat carry that label in products or in literature external to thisdocument.

It is contemplated that any number and type of components may be addedto and/or removed from de-centralized mechanism 610 to facilitatevarious embodiments including adding, removing, and/or enhancing certainfeatures. For brevity, clarity, and ease of understanding ofde-centralized mechanism 610, many of the standard and/or knowncomponents, such as those of a computing device, are not shown ordiscussed here. It is contemplated that embodiments, as describedherein, are not limited to any particular technology, topology, system,architecture, and/or standard and are dynamic enough to adopt and adaptto any future changes.

FIG. 8 illustrates a conventional framework 800 employing a centralizedscheduler 801. As previously discussed, conventional techniques arelimited to hosting and using centralized scheduler 801 to performload-balancing of tasks for slices 805A, 805B, graphics processor 803,etc. Given the centrality and remoteness of centralized scheduler 801,it remains unaware of local realities and thus, when load-balancingissues are encountered, centralized scheduler 801 is not equipped toaddress or handle them, resulting in loss of balance and inefficiencies.

FIG. 9A illustrates a novel architectural placement 900 employing ade-centralized load-balancing mechanism 610 according to one embodiment.For brevity, many of the details previously discussed with reference toFIGS. 1-8 may not be discussed or repeated hereafter. Any processesrelating to architectural placement 900 may be performed by processinglogic that may comprise hardware (e.g., circuitry, dedicated logic,programmable logic, etc.), software (such as instructions run on aprocessing device), or a combination thereof, as facilitated byde-centralized mechanism 610. The processes associated witharchitectural placement 900 may be illustrated or recited in linearsequences for brevity and clarity in presentation; however, it iscontemplated that any number of them can be performed in parallel,asynchronously, or in different orders.

As previously discussed with reference to FIG. 7, in one embodiment,de-centralized mechanism 610 is placed at each of graphics processors614, 914 and where de-centralized mechanism 610 serves as a localscheduler that works with other local components, such as copy engines905A, 905B to perform local load-balancing that is accurate andefficient as opposed to using centralized scheduler 801 of FIG. 8. Forbrevity, many of the details discussed with reference to FIG. 7 are notrepeated here.

As further discussed with reference to FIG. 7, in one embodiment, thisnovel technique of localized load-balancing may apply to slices 901A,901B, 903A, 903B, representing SMs, SMMs, etc., or directly to graphicsprocessors 614, 914 such that local load-balancing is performed usingde-centralized mechanism 610 and any essential data, information, etc.,is communicated and migrated between graphics processors 614 and 914and/or slices 901A, 903A and 901B, 903B, respectively, using a locallink, such as local bus 907.

FIG. 9B illustrates a novel architectural placement 950 employing ade-centralized load-balancing mechanism 610 according to one embodiment.For brevity, many of the details previously discussed with reference toFIGS. 1-9A may not be discussed or repeated hereafter. Any processesrelating to architectural placement 950 may be performed by processinglogic that may comprise hardware (e.g., circuitry, dedicated logic,programmable logic, etc.), software (such as instructions run on aprocessing device), or a combination thereof, as facilitated byde-centralized mechanism 610. The processes associated witharchitectural placement 950 may be illustrated or recited in linearsequences for brevity and clarity in presentation; however, it iscontemplated that any number of them can be performed in parallel,asynchronously, or in different orders.

As illustrated, graphics processor 614 includes multiple SMs/SMMs 901A,903A, where each of SMMs 901A, 903A includes a set of hardware threads,barriers, 953A, 953B, SLMs 951A, 951B, and/or local caches 955A, 955B.In one embodiment, as illustrated, de-centralized mechanism 610 may becommon to all SMMs 901A, 903A and responsible for scheduling new threadsor thread groups to the SMM hardware threads of SMMs 901A, 903A throughand using hardware engine for thread state migration (“thread engine”)957. In one embodiment, de-centralized mechanism 610 is further tomonitor or keep track of all the resource allocations in SMMs 901A,903A, including SLMs 951A, 951B, barriers 953A, 953B, etc.

For example, using detection/migration logic 701 of FIG. 7,de-centralized mechanism 610 periodically (or continuously or on-demand)monitor the hardware resource utilization, such as thread utilizationper SMM 901A, 903A, SLM space allocated and free per SMM 901A, 903A anda number of free barriers 953A, 953B per SMM 951A, 951B.

In one embodiment, de-centralized mechanism 610 may perform thefollowing: if detected by detection/migration logic 701 of FIG. 7 thatthe hardware thread utilization across SMMs 951A, 951B is significantlyout-of-balance, it may trigger migration of one or more threads orthread groups from one SMM to another, such as from 901A to 903A, tore-balance the load. To achieve this, the local hardware may be used tosupport a checkpoint-and-migration mechanism as facilitated bydetection/migration logic 701 of FIG. 7. Further, a new fixed-functionunit may be added to save the thread's state (e.g., hardware registers,SLMs 951A, 951B, barriers 953A, 953B, etc.) of one thread or threadgroup running in SMM 901A and reload that state in a different SMM 903A.

Further, in one embodiment, as facilitated by detection/migration logic701 of FIG. 7, de-centralized mechanism 610 detects that the SLM spacein one or more SMMs 901A, 903A is significantly fragmented, which mightprevent allocation of any new threads or thread groups, de-centralizedmechanism 610 may perform a de-fragmentation action such that allthreads in a thread group may be temporarily halted, while the SLM baseaddress (such as stored in a hardware register) may be updated and then,the threads may be restarted.

FIG. 9C illustrates SLM de-fragmentation operation 970 according to oneembodiment. For example, as illustrated, fragmented SLM 971 shows aspace that is not continuous, such as TG0 973 of 16 KB, followed by freespace of 8 KB, followed by TG1 975 of 16 KB, followed by another freespace of 8 KB, followed by TG 2 977 of 16 KB, followed by another freespace of 16 KB, and so forth. In one embodiment, usingscheduling/loading logic 705 of de-centralized mechanism 610, ade-fragmentation operation is triggered such that in the illustratedembodiment, TG1 973 and TG2 975 of SLM 971 are moved up or migrated tocreate a continuous 32 KB space in SLM 981, where TG0 971, TG1 973, TG2975 are continuous without any of the free space between them, leadingto new free space 989 of 32 KB where a new TG of 32 KB may be scheduledby scheduling/loading logic 705. In other words, without this noveltechnique for facilitating de-fragmentation operation, this arrangementwould not be feasible.

This novel technique has been described above with reference to specificembodiments. Persons skilled in the art, however, will understand thatvarious modifications and changes may be made thereto without departingfrom the broader spirit and scope as set forth in the appended claims.The foregoing description and drawings are, accordingly, to be regardedin an illustrative rather than a restrictive sense.

System Overview II

FIG. 10 is a block diagram of a processing system 1000, according to anembodiment. In various embodiments, the system 1000 includes one or moreprocessors 1002 and one or more graphics processors 1008, and may be asingle processor desktop system, a multiprocessor workstation system, ora server system having a large number of processors 1002 or processorcores 1007. In on embodiment, the system 1000 is a processing platformincorporated within a system-on-a-chip (SoC) integrated circuit for usein mobile, handheld, or embedded devices.

An embodiment of system 1000 can include, or be incorporated within aserver-based gaming platform, a game console, including a game and mediaconsole, a mobile gaming console, a handheld game console, or an onlinegame console. In some embodiments system 1000 is a mobile phone, smartphone, tablet computing device or mobile Internet device. Dataprocessing system 1000 can also include, couple with, or be integratedwithin a wearable device, such as a smart watch wearable device, smarteyewear device, augmented reality device, or virtual reality device. Insome embodiments, data processing system 1000 is a television or set topbox device having one or more processors 1002 and a graphical interfacegenerated by one or more graphics processors 1008.

In some embodiments, the one or more processors 1002 each include one ormore processor cores 1007 to process instructions which, when executed,perform operations for system and user software. In some embodiments,each of the one or more processor cores 1007 is configured to process aspecific instruction set 1009. In some embodiments, instruction set 1009may facilitate Complex Instruction Set Computing (CISC), ReducedInstruction Set Computing (RISC), or computing via a Very LongInstruction Word (VLIW). Multiple processor cores 1007 may each processa different instruction set 1009, which may include instructions tofacilitate the emulation of other instruction sets. Processor core 1007may also include other processing devices, such a Digital SignalProcessor (DSP).

In some embodiments, the processor 1002 includes cache memory 1004.Depending on the architecture, the processor 1002 can have a singleinternal cache or multiple levels of internal cache. In someembodiments, the cache memory is shared among various components of theprocessor 1002. In some embodiments, the processor 1002 also uses anexternal cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC))(not shown), which may be shared among processor cores 1007 using knowncache coherency techniques. A register file 1006 is additionallyincluded in processor 1002 which may include different types ofregisters for storing different types of data (e.g., integer registers,floating point registers, status registers, and an instruction pointerregister). Some registers may be general-purpose registers, while otherregisters may be specific to the design of the processor 1002.

In some embodiments, processor 1002 is coupled to a processor bus 1010to transmit communication signals such as address, data, or controlsignals between processor 1002 and other components in system 1000. Inone embodiment, the system 1000 uses an exemplary ‘hub’ systemarchitecture, including a memory controller hub 1016 and an Input Output(I/O) controller hub 1030. A memory controller hub 1016 facilitatescommunication between a memory device and other components of system1000, while an I/O Controller Hub (ICH) 1030 provides connections to I/Odevices via a local I/O bus. In one embodiment, the logic of the memorycontroller hub 1016 is integrated within the processor.

Memory device 1020 can be a dynamic random access memory (DRAM) device,a static random access memory (SRAM) device, flash memory device,phase-change memory device, or some other memory device having suitableperformance to serve as process memory. In one embodiment, the memorydevice 1020 can operate as system memory for the system 1000, to storedata 1022 and instructions 1021 for use when the one or more processors1002 executes an application or process. Memory controller hub 1016 alsocouples with an optional external graphics processor 1012, which maycommunicate with the one or more graphics processors 1008 in processors1002 to perform graphics and media operations.

In some embodiments, ICH 1030 enables peripherals to connect to memorydevice 1020 and processor 1002 via a high-speed I/O bus. The I/Operipherals include, but are not limited to, an audio controller 1046, afirmware interface 1028, a wireless transceiver 1026 (e.g., Wi-Fi,Bluetooth), a data storage device 1024 (e.g., hard disk drive, flashmemory, etc.), and a legacy I/O controller 1040 for coupling legacy(e.g., Personal System 2 (PS/2)) devices to the system. One or moreUniversal Serial Bus (USB) controllers 1042 connect input devices, suchas keyboard and mouse 1044 combinations. A network controller 1034 mayalso couple to ICH 1030. In some embodiments, a high-performance networkcontroller (not shown) couples to processor bus 1010. It will beappreciated that the system 1000 shown is exemplary and not limiting, asother types of data processing systems that are differently configuredmay also be used. For example, the I/O controller hub 1030 may beintegrated within the one or more processor 1002, or the memorycontroller hub 1016 and I/O controller hub 1030 may be integrated into adiscreet external graphics processor, such as the external graphicsprocessor 1012.

FIG. 11 is a block diagram of an embodiment of a processor 1100 havingone or more processor cores 1102A-1102N, an integrated memory controller1114, and an integrated graphics processor 1108. Those elements of FIG.11 having the same reference numbers (or names) as the elements of anyother figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such. Processor1100 can include additional cores up to and including additional core1102N represented by the dashed lined boxes. Each of processor cores1102A-1102N includes one or more internal cache units 1104A-1104N. Insome embodiments, each processor core also has access to one or moreshared cached units 1106.

The internal cache units 1104A-1104N and shared cache units 1106represent a cache memory hierarchy within the processor 1100. The cachememory hierarchy may include at least one level of instruction and datacache within each processor core and one or more levels of sharedmid-level cache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), orother levels of cache, where the highest level of cache before externalmemory is classified as the LLC. In some embodiments, cache coherencylogic maintains coherency between the various cache units 1106 and1104A-1104N.

In some embodiments, processor 1100 may also include a set of one ormore bus controller units 1116 and a system agent core 1110. The one ormore bus controller units 1116 manage a set of peripheral buses, such asone or more Peripheral Component Interconnect buses (e.g., PCI, PCIExpress). System agent core 1110 provides management functionality forthe various processor components. In some embodiments, system agent core1110 includes one or more integrated memory controllers 1114 to manageaccess to various external memory devices (not shown).

In some embodiments, one or more of the processor cores 1102A-1102Ninclude support for simultaneous multi-threading. In such embodiment,the system agent core 1110 includes components for coordinating andoperating cores 1102A-1102N during multi-threaded processing. Systemagent core 1110 may additionally include a power control unit (PCU),which includes logic and components to regulate the power state ofprocessor cores 1102A-1102N and graphics processor 1108.

In some embodiments, processor 1100 additionally includes graphicsprocessor 1108 to execute graphics processing operations. In someembodiments, the graphics processor 1108 couples with the set of sharedcache units 1106, and the system agent core 1110, including the one ormore integrated memory controllers 1114. In some embodiments, a displaycontroller 1111 is coupled with the graphics processor 1108 to drivegraphics processor output to one or more coupled displays. In someembodiments, display controller 1111 may be a separate module coupledwith the graphics processor via at least one interconnect, or may beintegrated within the graphics processor 1108 or system agent core 1110.

In some embodiments, a ring based interconnect unit 1112 is used tocouple the internal components of the processor 1100. However, analternative interconnect unit may be used, such as a point-to-pointinterconnect, a switched interconnect, or other techniques, includingtechniques well known in the art. In some embodiments, graphicsprocessor 1108 couples with the ring interconnect 1112 via an I/O link1113.

The exemplary I/O link 1113 represents at least one of multiplevarieties of I/O interconnects, including an on-package I/O interconnectwhich facilitates communication between various processor components anda high-performance embedded memory module 1118, such as an eDRAM module.In some embodiments, each of the processor cores 1102-1102N and graphicsprocessor 1108 use embedded memory modules 1118 as a shared Last LevelCache.

In some embodiments, processor cores 1102A-1102N are homogenous coresexecuting the same instruction set architecture. In another embodiment,processor cores 1102A-1102N are heterogeneous in terms of instructionset architecture (ISA), where one or more of processor cores 1102A-Nexecute a first instruction set, while at least one of the other coresexecutes a subset of the first instruction set or a differentinstruction set. In one embodiment processor cores 1102A-1102N areheterogeneous in terms of microarchitecture, where one or more coreshaving a relatively higher power consumption couple with one or morepower cores having a lower power consumption. Additionally, processor1100 can be implemented on one or more chips or as an SoC integratedcircuit having the illustrated components, in addition to othercomponents.

FIG. 12 is a block diagram of a graphics processor 1200, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores. In some embodiments,the graphics processor communicates via a memory mapped I/O interface toregisters on the graphics processor and with commands placed into theprocessor memory. In some embodiments, graphics processor 1200 includesa memory interface 1214 to access memory. Memory interface 1214 can bean interface to local memory, one or more internal caches, one or moreshared external caches, and/or to system memory.

In some embodiments, graphics processor 1200 also includes a displaycontroller 1202 to drive display output data to a display device 1220.Display controller 1202 includes hardware for one or more overlay planesfor the display and composition of multiple layers of video or userinterface elements. In some embodiments, graphics processor 1200includes a video codec engine 1206 to encode, decode, or transcode mediato, from, or between one or more media encoding formats, including, butnot limited to Moving Picture Experts Group (MPEG) formats such asMPEG-2, Advanced Video Coding (AVC) formats such as H.264/MPEG-4 AVC, aswell as the Society of Motion Picture & Television Engineers (SMPTE)421M/VC-1, and Joint Photographic Experts Group (JPEG) formats such asJPEG, and Motion JPEG (MJPEG) formats.

In some embodiments, graphics processor 1200 includes a block imagetransfer (BLIT) engine 1204 to perform two-dimensional (2D) rasterizeroperations including, for example, bit-boundary block transfers.However, in one embodiment, 2D graphics operations are performed usingone or more components of graphics processing engine (GPE) 1210. In someembodiments, graphics processing engine 1210 is a compute engine forperforming graphics operations, including three-dimensional (3D)graphics operations and media operations.

In some embodiments, GPE 1210 includes a 3D pipeline 1212 for performing3D operations, such as rendering three-dimensional images and scenesusing processing functions that act upon 3D primitive shapes (e.g.,rectangle, triangle, etc.). The 3D pipeline 1212 includes programmableand fixed function elements that perform various tasks within theelement and/or spawn execution threads to a 3D/Media sub-system 1215.While 3D pipeline 1212 can be used to perform media operations, anembodiment of GPE 1210 also includes a media pipeline 1216 that isspecifically used to perform media operations, such as videopost-processing and image enhancement.

In some embodiments, media pipeline 1216 includes fixed function orprogrammable logic units to perform one or more specialized mediaoperations, such as video decode acceleration, video de-interlacing, andvideo encode acceleration in place of, or on behalf of video codecengine 1206. In some embodiments, media pipeline 1216 additionallyincludes a thread spawning unit to spawn threads for execution on3D/Media sub-system 1215. The spawned threads perform computations forthe media operations on one or more graphics execution units included in3D/Media sub-system 1215.

In some embodiments, 3D/Media subsystem 1215 includes logic forexecuting threads spawned by 3D pipeline 1212 and media pipeline 1216.In one embodiment, the pipelines send thread execution requests to3D/Media subsystem 1215, which includes thread dispatch logic forarbitrating and dispatching the various requests to available threadexecution resources. The execution resources include an array ofgraphics execution units to process the 3D and media threads. In someembodiments, 3D/Media subsystem 1215 includes one or more internalcaches for thread instructions and data. In some embodiments, thesubsystem also includes shared memory, including registers andaddressable memory, to share data between threads and to store outputdata.

3D/Media Processing

FIG. 13 is a block diagram of a graphics processing engine 1310 of agraphics processor in accordance with some embodiments. In oneembodiment, the graphics processing engine (GPE) 1310 is a version ofthe GPE 1210 shown in FIG. 12. Elements of FIG. 13 having the samereference numbers (or names) as the elements of any other figure hereincan operate or function in any manner similar to that describedelsewhere herein, but are not limited to such. For example, the 3Dpipeline 1212 and media pipeline 1216 of FIG. 12 are illustrated. Themedia pipeline 1216 is optional in some embodiments of the GPE 1310 andmay not be explicitly included within the GPE 1310. For example, and inat least one embodiment, a separate media and/or image processor iscoupled to the GPE 1310.

In some embodiments, GPE 1310 couples with or includes a commandstreamer 1303, which provides a command stream to the 3D pipeline 1212and/or media pipelines 1216. In some embodiments, command streamer 1303is coupled with memory, which can be system memory, or one or more ofinternal cache memory and shared cache memory. In some embodiments,command streamer 1303 receives commands from the memory and sends thecommands to 3D pipeline 1212 and/or media pipeline 1216. The commandsare directives fetched from a ring buffer, which stores commands for the3D pipeline 1212 and media pipeline 1216. In one embodiment, the ringbuffer can additionally include batch command buffers storing batches ofmultiple commands. The commands for the 3D pipeline 1212 can alsoinclude references to data stored in memory, such as but not limited tovertex and geometry data for the 3D pipeline 1212 and/or image data andmemory objects for the media pipeline 1216. The 3D pipeline 1212 andmedia pipeline 1216 process the commands and data by performingoperations via logic within the respective pipelines or by dispatchingone or more execution threads to a graphics core array 1314.

In various embodiments, the 3D pipeline 1212 can execute one or moreshader programs, such as vertex shaders, geometry shaders, pixelshaders, fragment shaders, compute shaders, or other shader programs, byprocessing the instructions and dispatching execution threads to thegraphics core array 1314. The graphics core array 1314 provides aunified block of execution resources. Multi-purpose execution logic(e.g., execution units) within the graphic core array 1314 includessupport for various 3D API shader languages and can execute multiplesimultaneous execution threads associated with multiple shaders.

In some embodiments, the graphics core array 1314 also includesexecution logic to perform media functions, such as video and/or imageprocessing. In one embodiment, the execution units additionally includegeneral-purpose logic that is programmable to perform parallel generalpurpose computational operations, in addition to graphics processingoperations. The general-purpose logic can perform processing operationsin parallel or in conjunction with general purpose logic within theprocessor core(s) 1007 of FIG. 10 or core 1102A-1102N as in FIG. 11.

Output data generated by threads executing on the graphics core array1314 can output data to memory in a unified return buffer (URB) 1318.The URB 1318 can store data for multiple threads. In some embodiments,the URB 1318 may be used to send data between different threadsexecuting on the graphics core array 1314. In some embodiments, the URB1318 may additionally be used for synchronization between threads on thegraphics core array and fixed function logic within the shared functionlogic 1320.

In some embodiments, graphics core array 1314 is scalable, such that thearray includes a variable number of graphics cores, each having avariable number of execution units based on the target power andperformance level of GPE 1310. In one embodiment, the executionresources are dynamically scalable, such that execution resources may beenabled or disabled as needed.

The graphics core array 1314 couples with shared function logic 1320that includes multiple resources that are shared between the graphicscores in the graphics core array. The shared functions within the sharedfunction logic 1320 are hardware logic units that provide specializedsupplemental functionality to the graphics core array 1314. In variousembodiments, shared function logic 1320 includes but is not limited tosampler 1321, math 1322, and inter-thread communication (ITC) 1323logic. Additionally, some embodiments implement one or more cache(s)1325 within the shared function logic 1320. A shared function isimplemented where the demand for a given specialized function isinsufficient for inclusion within the graphics core array 1314. Insteada single instantiation of that specialized function is implemented as astand-alone entity in the shared function logic 1320 and shared amongthe execution resources within the graphics core array 1314. The preciseset of functions that are shared between the graphics core array 1314and included within the graphics core array 1314 varies betweenembodiments.

FIG. 14 is a block diagram of another embodiment of a graphics processor1400. Elements of FIG. 14 having the same reference numbers (or names)as the elements of any other figure herein can operate or function inany manner similar to that described elsewhere herein, but are notlimited to such.

In some embodiments, graphics processor 1400 includes a ringinterconnect 1402, a pipeline front-end 1404, a media engine 1437, andgraphics cores 1480A-1480N. In some embodiments, ring interconnect 1402couples the graphics processor to other processing units, includingother graphics processors or one or more general-purpose processorcores. In some embodiments, the graphics processor is one of manyprocessors integrated within a multi-core processing system.

In some embodiments, graphics processor 1400 receives batches ofcommands via ring interconnect 1402. The incoming commands areinterpreted by a command streamer 1403 in the pipeline front-end 1404.In some embodiments, graphics processor 1400 includes scalable executionlogic to perform 3D geometry processing and media processing via thegraphics core(s) 1480A-1480N. For 3D geometry processing commands,command streamer 1403 supplies commands to geometry pipeline 1436. Forat least some media processing commands, command streamer 1403 suppliesthe commands to a video front end 1434, which couples with a mediaengine 1437. In some embodiments, media engine 1437 includes a VideoQuality Engine (VQE) 1430 for video and image post-processing and amulti-format encode/decode (MFX) 1433 engine to providehardware-accelerated media data encode and decode. In some embodiments,geometry pipeline 1436 and media engine 1437 each generate executionthreads for the thread execution resources provided by at least onegraphics core 1480A.

In some embodiments, graphics processor 1400 includes scalable threadexecution resources featuring modular cores 1480A-1480N (sometimesreferred to as core slices), each having multiple sub-cores 1450A-1450N,1460A-1460N (sometimes referred to as core sub-slices). In someembodiments, graphics processor 1400 can have any number of graphicscores 1480A through 1480N. In some embodiments, graphics processor 1400includes a graphics core 1480A having at least a first sub-core 1450Aand a second core sub-core 1460A. In other embodiments, the graphicsprocessor is a low power processor with a single sub-core (e.g., 1450A).In some embodiments, graphics processor 1400 includes multiple graphicscores 1480A-1480N, each including a set of first sub-cores 1450A-1450Nand a set of second sub-cores 1460A-1460N. Each sub-core in the set offirst sub-cores 1450A-1450N includes at least a first set of executionunits 1452A-1452N and media/texture samplers 1454A-1454N. Each sub-corein the set of second sub-cores 1460A-1460N includes at least a secondset of execution units 1462A-1462N and samplers 1464A-1464N. In someembodiments, each sub-core 1450A-1450N, 1460A-1460N shares a set ofshared resources 1470A-1470N. In some embodiments, the shared resourcesinclude shared cache memory and pixel operation logic. Other sharedresources may also be included in the various embodiments of thegraphics processor.

Execution Logic

FIG. 15 illustrates thread execution logic 1500 including an array ofprocessing elements employed in some embodiments of a GPE. Elements ofFIG. 15 having the same reference numbers (or names) as the elements ofany other figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such.

In some embodiments, thread execution logic 1500 includes a pixel shader1502, a thread dispatcher 1504, instruction cache 1506, a scalableexecution unit array including a plurality of execution units1508A-608N, a sampler 1510, a data cache 1512, and a data port 1514. Inone embodiment, the included components are interconnected via aninterconnect fabric that links to each of the components. In someembodiments, thread execution logic 1500 includes one or moreconnections to memory, such as system memory or cache memory, throughone or more of instruction cache 1506, data port 1514, sampler 1510, andexecution unit array 1508A-1508N. In some embodiments, each executionunit (e.g. 1508A) is an individual vector processor capable of executingmultiple simultaneous threads and processing multiple data elements inparallel for each thread. In some embodiments, execution unit array1508A-1508N includes any number individual execution units.

In some embodiments, execution unit array 1508A-1508N is primarily usedto execute “shader” programs. In some embodiments, the execution unitsin array 1508A-1508N execute an instruction set that includes nativesupport for many standard 3D graphics shader instructions, such thatshader programs from graphics libraries (e.g., Direct 3D and OpenGL) areexecuted with a minimal translation. The execution units support vertexand geometry processing (e.g., vertex programs, geometry programs,vertex shaders), pixel processing (e.g., pixel shaders, fragmentshaders) and general-purpose processing (e.g., compute and mediashaders).

Each execution unit in execution unit array 1508A-1508N operates onarrays of data elements. The number of data elements is the “executionsize,” or the number of channels for the instruction. An executionchannel is a logical unit of execution for data element access, masking,and flow control within instructions. The number of channels may beindependent of the number of physical Arithmetic Logic Units (ALUs) orFloating Point Units (FPUs) for a particular graphics processor. In someembodiments, execution units 1508A-1508N support integer andfloating-point data types.

The execution unit instruction set includes single instruction multipledata (SIMD) or single instruction multiple thread (SIMT) instructions.The various data elements can be stored as a packed data type in aregister and the execution unit will process the various elements basedon the data size of the elements. For example, when operating on a256-bit wide vector, the 256 bits of the vector are stored in a registerand the execution unit operates on the vector as four separate 64-bitpacked data elements (Quad-Word (QW) size data elements), eight separate32-bit packed data elements (Double Word (DW) size data elements),sixteen separate 16-bit packed data elements (Word (W) size dataelements), or thirty-two separate 8-bit data elements (byte (B) sizedata elements). However, different vector widths and register sizes arepossible.

One or more internal instruction caches (e.g., 1506) are included in thethread execution logic 1500 to cache thread instructions for theexecution units. In some embodiments, one or more data caches (e.g.,1512) are included to cache thread data during thread execution. In someembodiments, sampler 1510 is included to provide texture sampling for 3Doperations and media sampling for media operations. In some embodiments,sampler 1510 includes specialized texture or media samplingfunctionality to process texture or media data during the samplingprocess before providing the sampled data to an execution unit.

During execution, the graphics and media pipelines send threadinitiation requests to thread execution logic 1500 via thread spawningand dispatch logic. In some embodiments, thread execution logic 1500includes a local thread dispatcher 1504 that arbitrates threadinitiation requests from the graphics and media pipelines andinstantiates the requested threads on one or more execution units1508A-1508N. For example, the geometry pipeline (e.g., 1436 of FIG. 14)dispatches vertex processing, tessellation, or geometry processingthreads to thread execution logic 1500 (FIG. 15). In some embodiments,thread dispatcher 1504 can also process runtime thread spawning requestsfrom the executing shader programs.

Once a group of geometric objects has been processed and rasterized intopixel data, pixel shader 1502 is invoked to further compute outputinformation and cause results to be written to output surfaces (e.g.,color buffers, depth buffers, stencil buffers, etc.). In someembodiments, pixel shader 1502 calculates the values of the variousvertex attributes that are to be interpolated across the rasterizedobject. In some embodiments, pixel shader 1502 then executes anapplication programming interface (API)-supplied pixel shader program.To execute the pixel shader program, pixel shader 1502 dispatchesthreads to an execution unit (e.g., 1508A) via thread dispatcher 1504.In some embodiments, pixel shader 1502 uses texture sampling logic insampler 1510 to access texture data in texture maps stored in memory.Arithmetic operations on the texture data and the input geometry datacompute pixel color data for each geometric fragment, or discards one ormore pixels from further processing.

In some embodiments, the data port 1514 provides a memory accessmechanism for the thread execution logic 1500 output processed data tomemory for processing on a graphics processor output pipeline. In someembodiments, the data port 1514 includes or couples to one or more cachememories (e.g., data cache 1512) to cache data for memory access via thedata port.

FIG. 16 is a block diagram illustrating a graphics processor instructionformats 1600 according to some embodiments. In one or more embodiment,the graphics processor execution units support an instruction set havinginstructions in multiple formats. The solid lined boxes illustrate thecomponents that are generally included in an execution unit instruction,while the dashed lines include components that are optional or that areonly included in a sub-set of the instructions. In some embodiments,instruction format 1600 described and illustrated aremacro-instructions, in that they are instructions supplied to theexecution unit, as opposed to micro-operations resulting frominstruction decode once the instruction is processed.

In some embodiments, the graphics processor execution units nativelysupport instructions in a 128-bit instruction format 1610. A 64-bitcompacted instruction format 1630 is available for some instructionsbased on the selected instruction, instruction options, and number ofoperands. The native 128-bit instruction format 1610 provides access toall instruction options, while some options and operations arerestricted in the 64-bit instruction format 1630. The nativeinstructions available in the 64-bit instruction format 1630 vary byembodiment. In some embodiments, the instruction is compacted in partusing a set of index values in an index field 1613. The execution unithardware references a set of compaction tables based on the index valuesand uses the compaction table outputs to reconstruct a nativeinstruction in the 128-bit instruction format 1610.

For each format, instruction opcode 1612 defines the operation that theexecution unit is to perform. The execution units execute eachinstruction in parallel across the multiple data elements of eachoperand. For example, in response to an add instruction the executionunit performs a simultaneous add operation across each color channelrepresenting a texture element or picture element. By default, theexecution unit performs each instruction across all data channels of theoperands. In some embodiments, instruction control field 1614 enablescontrol over certain execution options, such as channels selection(e.g., predication) and data channel order (e.g., swizzle). For 128-bitinstructions 1610 an exec-size field 1616 limits the number of datachannels that will be executed in parallel. In some embodiments,exec-size field 1616 is not available for use in the 64-bit compactinstruction format 1630.

Some execution unit instructions have up to three operands including twosource operands, src0 1620, src1 1622, and one destination 1618. In someembodiments, the execution units support dual destination instructions,where one of the destinations is implied. Data manipulation instructionscan have a third source operand (e.g., SRC2 1624), where the instructionopcode 1612 determines the number of source operands. An instruction'slast source operand can be an immediate (e.g., hard-coded) value passedwith the instruction.

In some embodiments, the 128-bit instruction format 1610 includes anaccess/address mode information 1626 specifying, for example, whetherdirect register addressing mode or indirect register addressing mode isused. When direct register addressing mode is used, the register addressof one or more operands is directly provided by bits in the instruction1610.

In some embodiments, the 128-bit instruction format 1610 includes anaccess/address mode field 1626, which specifies an address mode and/oran access mode for the instruction. In one embodiment, the access modeto define a data access alignment for the instruction. Some embodimentssupport access modes including a 16-byte aligned access mode and a1-byte aligned access mode, where the byte alignment of the access modedetermines the access alignment of the instruction operands. Forexample, when in a first mode, the instruction 1610 may use byte-alignedaddressing for source and destination operands and when in a secondmode, the instruction 1610 may use 16-byte-aligned addressing for allsource and destination operands.

In one embodiment, the address mode portion of the access/address modefield 1626 determines whether the instruction is to use direct orindirect addressing. When direct register addressing mode is used bitsin the instruction 1610 directly provide the register address of one ormore operands. When indirect register addressing mode is used, theregister address of one or more operands may be computed based on anaddress register value and an address immediate field in theinstruction.

In some embodiments, instructions are grouped based on opcode 1612bit-fields to simplify Opcode decode 1640. For an 8-bit opcode, bits 4,5, and 6 allow the execution unit to determine the type of opcode. Theprecise opcode grouping shown is merely an example. In some embodiments,a move and logic opcode group 1642 includes data movement and logicinstructions (e.g., move (mov), compare (cmp)). In some embodiments,move and logic group 1642 shares the five most significant bits (MSB),where move (mov) instructions are in the form of 0000xxxxb and logicinstructions are in the form of 0001xxxxb. A flow control instructiongroup 1644 (e.g., call, jump (jmp)) includes instructions in the form of0010xxxxb (e.g., 0x20). A miscellaneous instruction group 1646 includesa mix of instructions, including synchronization instructions (e.g.,wait, send) in the form of 0011xxxxb (e.g., 0x30). A parallel mathinstruction group 1648 includes component-wise arithmetic instructions(e.g., add, multiply (mul)) in the form of 0100xxxxb (e.g., 0x40). Theparallel math group 1648 performs the arithmetic operations in parallelacross data channels. The vector math group 1650 includes arithmeticinstructions (e.g., dp4) in the form of 0101xxxxb (e.g., 0x50). Thevector math group performs arithmetic such as dot product calculationson vector operands.

Graphics Pipeline

FIG. 17 is a block diagram of another embodiment of a graphics processor1700. Elements of FIG. 17 having the same reference numbers (or names)as the elements of any other figure herein can operate or function inany manner similar to that described elsewhere herein, but are notlimited to such.

In some embodiments, graphics processor 1700 includes a graphicspipeline 1720, a media pipeline 1730, a display engine 1740, threadexecution logic 1750, and a render output pipeline 1770. In someembodiments, graphics processor 1700 is a graphics processor within amulti-core processing system that includes one or more general-purposeprocessing cores. The graphics processor is controlled by registerwrites to one or more control registers (not shown) or via commandsissued to graphics processor 1700 via a ring interconnect 1702. In someembodiments, ring interconnect 1702 couples graphics processor 1700 toother processing components, such as other graphics processors orgeneral-purpose processors. Commands from ring interconnect 1702 areinterpreted by a command streamer 1703, which supplies instructions toindividual components of graphics pipeline 1720 or media pipeline 1730.

In some embodiments, command streamer 1703 directs the operation of avertex fetcher 1705 that reads vertex data from memory and executesvertex-processing commands provided by command streamer 1703. In someembodiments, vertex fetcher 1705 provides vertex data to a vertex shader1707, which performs coordinate space transformation and lightingoperations to each vertex. In some embodiments, vertex fetcher 1705 andvertex shader 1707 execute vertex-processing instructions by dispatchingexecution threads to execution units 1752A, 1752B via a threaddispatcher 1731.

In some embodiments, execution units 1752A, 1752B are an array of vectorprocessors having an instruction set for performing graphics and mediaoperations. In some embodiments, execution units 1752A, 1752B have anattached L1 cache 1751 that is specific for each array or shared betweenthe arrays. The cache can be configured as a data cache, an instructioncache, or a single cache that is partitioned to contain data andinstructions in different partitions.

In some embodiments, graphics pipeline 1720 includes tessellationcomponents to perform hardware-accelerated tessellation of 3D objects.In some embodiments, a programmable hull shader 1711 configures thetessellation operations. A programmable domain shader 1717 providesback-end evaluation of tessellation output. A tessellator 1713 operatesat the direction of hull shader 1711 and contains special purpose logicto generate a set of detailed geometric objects based on a coarsegeometric model that is provided as input to graphics pipeline 1720. Insome embodiments, if tessellation is not used, tessellation components1711, 1713, 1717 can be bypassed.

In some embodiments, complete geometric objects can be processed by ageometry shader 1719 via one or more threads dispatched to executionunits 1752A, 1752B, or can proceed directly to the clipper 1729. In someembodiments, the geometry shader operates on entire geometric objects,rather than vertices or patches of vertices as in previous stages of thegraphics pipeline. If the tessellation is disabled the geometry shader1719 receives input from the vertex shader 1707. In some embodiments,geometry shader 1719 is programmable by a geometry shader program toperform geometry tessellation if the tessellation units are disabled.

Before rasterization, a clipper 1729 processes vertex data. The clipper1729 may be a fixed function clipper or a programmable clipper havingclipping and geometry shader functions. In some embodiments, arasterizer and depth test component 1773 in the render output pipeline1770 dispatches pixel shaders to convert the geometric objects intotheir per pixel representations. In some embodiments, pixel shader logicis included in thread execution logic 1750. In some embodiments, anapplication can bypass rasterization and access un-rasterized vertexdata via a stream out unit 1723.

The graphics processor 1700 has an interconnect bus, interconnectfabric, or some other interconnect mechanism that allows data andmessage passing amongst the major components of the processor. In someembodiments, execution units 1752A, 1752B and associated cache(s) 1751,texture and media sampler 1754, and texture/sampler cache 1758interconnect via a data port 1756 to perform memory access andcommunicate with render output pipeline components of the processor. Insome embodiments, sampler 1754, caches 1751, 1758 and execution units1752A, 1752B each have separate memory access paths.

In some embodiments, render output pipeline 1770 contains a rasterizerand depth test component 1773 that converts vertex-based objects into anassociated pixel-based representation. In some embodiments, the renderoutput pipeline 1770 includes a windower/masker unit to perform fixedfunction triangle and line rasterization. An associated render cache1778 and depth cache 1779 are also available in some embodiments. Apixel operations component 1777 performs pixel-based operations on thedata, though in some instances, pixel operations associated with 2Doperations (e.g. bit block image transfers with blending) are performedby the 2D engine 1741, or substituted at display time by the displaycontroller 1743 using overlay display planes. In some embodiments, ashared L3 cache 1775 is available to all graphics components, allowingthe sharing of data without the use of main system memory.

In some embodiments, graphics processor media pipeline 1730 includes amedia engine 1737 and a video front end 1734. In some embodiments, videofront end 1734 receives pipeline commands from the command streamer1703. In some embodiments, media pipeline 1730 includes a separatecommand streamer. In some embodiments, video front-end 1734 processesmedia commands before sending the command to the media engine 1737. Insome embodiments, media engine 1737 includes thread spawningfunctionality to spawn threads for dispatch to thread execution logic1750 via thread dispatcher 1731.

In some embodiments, graphics processor 1700 includes a display engine1740. In some embodiments, display engine 1740 is external to processor1700 and couples with the graphics processor via the ring interconnect1702, or some other interconnect bus or fabric. In some embodiments,display engine 1740 includes a 2D engine 1741 and a display controller1743. In some embodiments, display engine 1740 contains special purposelogic capable of operating independently of the 3D pipeline. In someembodiments, display controller 1743 couples with a display device (notshown), which may be a system integrated display device, as in a laptopcomputer, or an external display device attached via a display deviceconnector.

In some embodiments, graphics pipeline 1720 and media pipeline 1730 areconfigurable to perform operations based on multiple graphics and mediaprogramming interfaces and are not specific to any one applicationprogramming interface (API). In some embodiments, driver software forthe graphics processor translates API calls that are specific to aparticular graphics or media library into commands that can be processedby the graphics processor. In some embodiments, support is provided forthe Open Graphics Library (OpenGL) and Open Computing Language (OpenCL)from the Khronos Group, the Direct3D library from the MicrosoftCorporation, or support may be provided to both OpenGL and D3D. Supportmay also be provided for the Open Source Computer Vision Library(OpenCV). A future API with a compatible 3D pipeline would also besupported if a mapping can be made from the pipeline of the future APIto the pipeline of the graphics processor.

Graphics Pipeline Programming

FIG. 18A is a block diagram illustrating a graphics processor commandformat 1800 according to some embodiments. FIG. 18B is a block diagramillustrating a graphics processor command sequence 1810 according to anembodiment. The solid lined boxes in FIG. 18A illustrate the componentsthat are generally included in a graphics command while the dashed linesinclude components that are optional or that are only included in asub-set of the graphics commands. The exemplary graphics processorcommand format 1800 of FIG. 18A includes data fields to identify atarget client 1802 of the command, a command operation code (opcode)1804, and the relevant data 1806 for the command. A sub-opcode 1805 anda command size 1808 are also included in some commands.

In some embodiments, client 1802 specifies the client unit of thegraphics device that processes the command data. In some embodiments, agraphics processor command parser examines the client field of eachcommand to condition the further processing of the command and route thecommand data to the appropriate client unit. In some embodiments, thegraphics processor client units include a memory interface unit, arender unit, a 2D unit, a 3D unit, and a media unit. Each client unithas a corresponding processing pipeline that processes the commands.Once the command is received by the client unit, the client unit readsthe opcode 1804 and, if present, sub-opcode 1805 to determine theoperation to perform. The client unit performs the command usinginformation in data field 1806. For some commands an explicit commandsize 1808 is expected to specify the size of the command. In someembodiments, the command parser automatically determines the size of atleast some of the commands based on the command opcode. In someembodiments, commands are aligned via multiples of a double word.

The flow diagram in FIG. 18B shows an exemplary graphics processorcommand sequence 1810. In some embodiments, software or firmware of adata processing system that features an embodiment of a graphicsprocessor uses a version of the command sequence shown to set up,execute, and terminate a set of graphics operations. A sample commandsequence is shown and described for purposes of example only asembodiments are not limited to these specific commands or to thiscommand sequence. Moreover, the commands may be issued as batch ofcommands in a command sequence, such that the graphics processor willprocess the sequence of commands in at least partially concurrence.

In some embodiments, the graphics processor command sequence 1810 maybegin with a pipeline flush command 1812 to cause any active graphicspipeline to complete the currently pending commands for the pipeline. Insome embodiments, the 3D pipeline 1822 and the media pipeline 1824 donot operate concurrently. The pipeline flush is performed to cause theactive graphics pipeline to complete any pending commands. In responseto a pipeline flush, the command parser for the graphics processor willpause command processing until the active drawing engines completepending operations and the relevant read caches are invalidated.Optionally, any data in the render cache that is marked ‘dirty’ can beflushed to memory. In some embodiments, pipeline flush command 1812 canbe used for pipeline synchronization or before placing the graphicsprocessor into a low power state.

In some embodiments, a pipeline select command 1813 is used when acommand sequence requires the graphics processor to explicitly switchbetween pipelines. In some embodiments, a pipeline select command 1813is required only once within an execution context before issuingpipeline commands unless the context is to issue commands for bothpipelines. In some embodiments, a pipeline flush command is 1812 isrequired immediately before a pipeline switch via the pipeline selectcommand 1813.

In some embodiments, a pipeline control command 1814 configures agraphics pipeline for operation and is used to program the 3D pipeline1822 and the media pipeline 1824. In some embodiments, pipeline controlcommand 1814 configures the pipeline state for the active pipeline. Inone embodiment, the pipeline control command 1814 is used for pipelinesynchronization and to clear data from one or more cache memories withinthe active pipeline before processing a batch of commands.

In some embodiments, commands for the return buffer state 1816 are usedto configure a set of return buffers for the respective pipelines towrite data. Some pipeline operations require the allocation, selection,or configuration of one or more return buffers into which the operationswrite intermediate data during processing. In some embodiments, thegraphics processor also uses one or more return buffers to store outputdata and to perform cross thread communication. In some embodiments,configuring the return buffer state 1816 includes selecting the size andnumber of return buffers to use for a set of pipeline operations.

The remaining commands in the command sequence differ based on theactive pipeline for operations. Based on a pipeline determination 1820,the command sequence is tailored to the 3D pipeline 1822 beginning withthe 3D pipeline state 1830, or the media pipeline 1824 beginning at themedia pipeline state 1840.

The commands for the 3D pipeline state 1830 include 3D state settingcommands for vertex buffer state, vertex element state, constant colorstate, depth buffer state, and other state variables that are to beconfigured before 3D primitive commands are processed. The values ofthese commands are determined at least in part based the particular 3DAPI in use. In some embodiments, 3D pipeline state 1830 commands arealso able to selectively disable or bypass certain pipeline elements ifthose elements will not be used.

In some embodiments, 3D primitive 1832 command is used to submit 3Dprimitives to be processed by the 3D pipeline. Commands and associatedparameters that are passed to the graphics processor via the 3Dprimitive 1832 command are forwarded to the vertex fetch function in thegraphics pipeline. The vertex fetch function uses the 3D primitive 1832command data to generate vertex data structures. The vertex datastructures are stored in one or more return buffers. In someembodiments, 3D primitive 1832 command is used to perform vertexoperations on 3D primitives via vertex shaders. To process vertexshaders, 3D pipeline 1822 dispatches shader execution threads tographics processor execution units.

In some embodiments, 3D pipeline 1822 is triggered via an execute 1834command or event. In some embodiments, a register write triggers commandexecution. In some embodiments execution is triggered via a ‘go’ or‘kick’ command in the command sequence. In one embodiment commandexecution is triggered using a pipeline synchronization command to flushthe command sequence through the graphics pipeline. The 3D pipeline willperform geometry processing for the 3D primitives. Once operations arecomplete, the resulting geometric objects are rasterized and the pixelengine colors the resulting pixels. Additional commands to control pixelshading and pixel back end operations may also be included for thoseoperations.

In some embodiments, the graphics processor command sequence 1810follows the media pipeline 1824 path when performing media operations.In general, the specific use and manner of programming for the mediapipeline 1824 depends on the media or compute operations to beperformed. Specific media decode operations may be offloaded to themedia pipeline during media decode. In some embodiments, the mediapipeline can also be bypassed and media decode can be performed in wholeor in part using resources provided by one or more general-purposeprocessing cores. In one embodiment, the media pipeline also includeselements for general-purpose graphics processor unit (GPGPU) operations,where the graphics processor is used to perform SIMD vector operationsusing computational shader programs that are not explicitly related tothe rendering of graphics primitives.

In some embodiments, media pipeline 1824 is configured in a similarmanner as the 3D pipeline 1822. A set of commands to configure the mediapipeline state 1840 are dispatched or placed into a command queue beforethe media object commands 1842. In some embodiments, commands for themedia pipeline state 1840 include data to configure the media pipelineelements that will be used to process the media objects. This includesdata to configure the video decode and video encode logic within themedia pipeline, such as encode or decode format. In some embodiments,commands for the media pipeline state 1840 also support the use of oneor more pointers to “indirect” state elements that contain a batch ofstate settings.

In some embodiments, media object commands 1842 supply pointers to mediaobjects for processing by the media pipeline. The media objects includememory buffers containing video data to be processed. In someembodiments, all media pipeline states must be valid before issuing amedia object command 1842. Once the pipeline state is configured andmedia object commands 1842 are queued, the media pipeline 1824 istriggered via an execute command 1844 or an equivalent execute event(e.g., register write). Output from media pipeline 1824 may then be postprocessed by operations provided by the 3D pipeline 1822 or the mediapipeline 1824. In some embodiments, GPGPU operations are configured andexecuted in a similar manner as media operations.

Graphics Software Architecture

FIG. 19 illustrates exemplary graphics software architecture for a dataprocessing system 1900 according to some embodiments. In someembodiments, software architecture includes a 3D graphics application1910, an operating system 1920, and at least one processor 1930. In someembodiments, processor 1930 includes a graphics processor 1932 and oneor more general-purpose processor core(s) 1934. The graphics application1910 and operating system 1920 each execute in the system memory 1950 ofthe data processing system.

In some embodiments, 3D graphics application 1910 contains one or moreshader programs including shader instructions 1912. The shader languageinstructions may be in a high-level shader language, such as the HighLevel Shader Language (HLSL) or the OpenGL Shader Language (GLSL). Theapplication also includes executable instructions 1914 in a machinelanguage suitable for execution by the general-purpose processor core(s)1934. The application also includes graphics objects 1916 defined byvertex data.

In some embodiments, operating system 1920 is a Microsoft® Windows®operating system from the Microsoft Corporation, a proprietary UNIX-likeoperating system, or an open source UNIX-like operating system using avariant of the Linux kernel. The operating system 1920 can support agraphics API 1922 such as the Direct3D API or the OpenGL API. When theDirect3D API is in use, the operating system 1920 uses a front-endshader compiler 1924 to compile any shader instructions 1912 in HLSLinto a lower-level shader language. The compilation may be ajust-in-time (JIT) compilation or the application can perform shaderpre-compilation. In some embodiments, high-level shaders are compiledinto low-level shaders during the compilation of the 3D graphicsapplication 1910.

In some embodiments, user mode graphics driver 1926 contains a back-endshader compiler 1927 to convert the shader instructions 1912 into ahardware specific representation. When the OpenGL API is in use, shaderinstructions 1912 in the GLSL high-level language are passed to a usermode graphics driver 1926 for compilation. In some embodiments, usermode graphics driver 1926 uses operating system kernel mode functions1928 to communicate with a kernel mode graphics driver 1929. In someembodiments, kernel mode graphics driver 1929 communicates with graphicsprocessor 1932 to dispatch commands and instructions.

IP Core Implementations

One or more aspects of at least one embodiment may be implemented byrepresentative code stored on a machine-readable medium which representsand/or defines logic within an integrated circuit such as a processor.For example, the machine-readable medium may include instructions whichrepresent various logic within the processor. When read by a machine,the instructions may cause the machine to fabricate the logic to performthe techniques described herein. Such representations, known as “IPcores,” are reusable units of logic for an integrated circuit that maybe stored on a tangible, machine-readable medium as a hardware modelthat describes the structure of the integrated circuit. The hardwaremodel may be supplied to various customers or manufacturing facilities,which load the hardware model on fabrication machines that manufacturethe integrated circuit. The integrated circuit may be fabricated suchthat the circuit performs operations described in association with anyof the embodiments described herein.

FIG. 20 is a block diagram illustrating an IP core development system2000 that may be used to manufacture an integrated circuit to performoperations according to an embodiment. The IP core development system2000 may be used to generate modular, re-usable designs that can beincorporated into a larger design or used to construct an entireintegrated circuit (e.g., an SOC integrated circuit). A design facility2030 can generate a software simulation 2010 of an IP core design in ahigh-level programming language (e.g., C/C++). The software simulation2010 can be used to design, test, and verify the behavior of the IP coreusing a simulation model 2012. The simulation model 2012 may includefunctional, behavioral, and/or timing simulations. A register transferlevel (RTL) design 2015 can then be created or synthesized from thesimulation model 2012. The RTL design 2015 is an abstraction of thebehavior of the integrated circuit that models the flow of digitalsignals between hardware registers, including the associated logicperformed using the modeled digital signals. In addition to an RTLdesign 2015, lower-level designs at the logic level or transistor levelmay also be created, designed, or synthesized. Thus, the particulardetails of the initial design and simulation may vary.

The RTL design 2015 or equivalent may be further synthesized by thedesign facility into a hardware model 2020, which may be in a hardwaredescription language (HDL), or some other representation of physicaldesign data. The HDL may be further simulated or tested to verify the IPcore design. The IP core design can be stored for delivery to a 3^(rd)party fabrication facility 2065 using non-volatile memory 2040 (e.g.,hard disk, flash memory, or any non-volatile storage medium).Alternatively, the IP core design may be transmitted (e.g., via theInternet) over a wired connection 2050 or wireless connection 2060. Thefabrication facility 2065 may then fabricate an integrated circuit thatis based at least in part on the IP core design. The fabricatedintegrated circuit can be configured to perform operations in accordancewith at least one embodiment described herein.

Exemplary System on a Chip Integrated Circuit

FIGS. 21-23 illustrate exemplary integrated circuits and associatedgraphics processors that may be fabricated using one or more IP cores,according to various embodiments described herein. In addition to whatis illustrated, other logic and circuits may be included, includingadditional graphics processors/cores, peripheral interface controllers,or general purpose processor cores.

FIG. 21 is a block diagram illustrating an exemplary system on a chipintegrated circuit 2100 that may be fabricated using one or more IPcores, according to an embodiment. Exemplary integrated circuit 2100includes one or more application processor(s) 2105 (e.g., CPUs), atleast one graphics processor 2110, and may additionally include an imageprocessor 2115 and/or a video processor 2120, any of which may be amodular IP core from the same or multiple different design facilities.Integrated circuit 2100 includes peripheral or bus logic including a USBcontroller 2125, UART controller 2130, an SPI/SDIO controller 2135, andan I²S/I²C controller 2140. Additionally, the integrated circuit caninclude a display device 2145 coupled to one or more of ahigh-definition multimedia interface (HDMI) controller 2150 and a mobileindustry processor interface (MIPI) display interface 2155. Storage maybe provided by a flash memory subsystem 2160 including flash memory anda flash memory controller. Memory interface may be provided via a memorycontroller 2165 for access to SDRAM or SRAM memory devices. Someintegrated circuits additionally include an embedded security engine2170.

FIG. 22 is a block diagram illustrating an exemplary graphics processor2210 of a system on a chip integrated circuit that may be fabricatedusing one or more IP cores, according to an embodiment. Graphicsprocessor 2210 can be a variant of the graphics processor 2110 of FIG.21. Graphics processor 2210 includes a vertex processor 2205 and one ormore fragment processor(s) 2215A-2215N (e.g., 2215A, 2215B, 2215C,2215D, through 2215N−1, and 2215N). Graphics processor 2210 can executedifferent shader programs via separate logic, such that the vertexprocessor 2205 is optimized to execute operations for vertex shaderprograms, while the one or more fragment processor(s) 2215A-2215Nexecute fragment (e.g., pixel) shading operations for fragment or pixelshader programs. The vertex processor 2205 performs the vertexprocessing stage of the 3D graphics pipeline and generates primitivesand vertex data. The fragment processor(s) 2215A-2215N use the primitiveand vertex data generated by the vertex processor 2205 to produce aframebuffer that is displayed on a display device. In one embodiment,the fragment processor(s) 2215A-2215N are optimized to execute fragmentshader programs as provided for in the OpenGL API, which may be used toperform similar operations as a pixel shader program as provided for inthe Direct 3D API.

Graphics processor 2210 additionally includes one or more memorymanagement units (MMUs) 2220A-2220B, cache(s) 2225A-2225B, and circuitinterconnect(s) 2230A-2230B. The one or more MMU(s) 2220A-2220B providefor virtual to physical address mapping for graphics processor 2210,including for the vertex processor 2205 and/or fragment processor(s)2215A-2215N, which may reference vertex or image/texture data stored inmemory, in addition to vertex or image/texture data stored in the one ormore cache(s) 2225A-2225B. In one embodiment, the one or more MMU(s)2220A-2220B may be synchronized with other MMUs within the system,including one or more MMUs associated with the one or more applicationprocessor(s) 2105, image processor 2115, and/or video processor 2120 ofFIG. 21, such that each processor 2105-2120 can participate in a sharedor unified virtual memory system. The one or more circuitinterconnect(s) 2230A-2230B enable graphics processor 2210 to interfacewith other IP cores within the SoC, either via an internal bus of theSoC or via a direct connection, according to embodiments.

FIG. 23 is a block diagram illustrating an additional exemplary graphicsprocessor 2310 of a system on a chip integrated circuit that may befabricated using one or more IP cores, according to an embodiment.Graphics processor 2310 can be a variant of the graphics processor 2110of FIG. 21. Graphics processor 2310 includes the one or more MMU(s)2220A-2220B, cache(s) 2225A-2225B, and circuit interconnect(s)2230A-2230B of the integrated circuit 2200 of FIG. 22.

Graphics processor 2310 includes one or more shader core(s) 2315A-2315N(e.g., 2315A, 2315B, 2315C, 2315D, 2315E, 2315F, through 2215N-1, and2215N), which provides for a unified shader core architecture in which asingle core or type or core can execute all types of programmable shadercode, including shader program code to implement vertex shaders,fragment shaders, and/or compute shaders. The exact number of shadercores present can vary among embodiments and implementations.Additionally, graphics processor 2310 includes an inter-core taskmanager 2305, which acts as a thread dispatcher to dispatch executionthreads to one or more shader core(s) 2315A-2315N. Graphics processor2310 additionally includes a tiling unit 2318 to accelerate tilingoperations for tile-based rendering, in which rendering operations for ascene are subdivided in image space. Tile-based rendering can be used toexploit local spatial coherence within a scene or to optimize use ofinternal caches.

References to “one embodiment”, “an embodiment”, “example embodiment”,“various embodiments”, etc., indicate that the embodiment(s) sodescribed may include particular features, structures, orcharacteristics, but not every embodiment necessarily includes theparticular features, structures, or characteristics. Further, someembodiments may have some, all, or none of the features described forother embodiments.

In the foregoing specification, embodiments have been described withreference to specific exemplary embodiments thereof. It will, however,be evident that various modifications and changes may be made theretowithout departing from the broader spirit and scope of embodiments asset forth in the appended claims. The Specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense.

In the following description and claims, the term “coupled” along withits derivatives, may be used. “Coupled” is used to indicate that two ormore elements co-operate or interact with each other, but they may ormay not have intervening physical or electrical components between them.

As used in the claims, unless otherwise specified the use of the ordinaladjectives “first”, “second”, “third”, etc., to describe a commonelement, merely indicate that different instances of like elements arebeing referred to, and are not intended to imply that the elements sodescribed must be in a given sequence, either temporally, spatially, inranking, or in any other manner.

The following clauses and/or examples pertain to further embodiments orexamples. Specifics in the examples may be used anywhere in one or moreembodiments. The various features of the different embodiments orexamples may be variously combined with some features included andothers excluded to suit a variety of different applications. Examplesmay include subject matter such as a method, means for performing actsof the method, at least one machine-readable medium includinginstructions that, when performed by a machine cause the machine toperforms acts of the method, or of an apparatus or system forfacilitating hybrid communication according to embodiments and examplesdescribed herein.

Some embodiments pertain to Example 1 that includes an apparatus tolocalized load-balancing for processors in computing devices, theapparatus comprising: a processor to host a local load-balancingmechanism; detection/migration logic of the local load-balancingmechanism to monitor balancing of loads at the processor; andscheduling/loading logic to serve as a local scheduler to maintainde-centralized load-balancing at the processor and between the processorand other one or more processors.

Example 2 includes the subject matter of Example 1, wherein thedetection/migration logic is further to detect lack of balance indistribution of the loads at the processor, wherein the lack of balanceis due to one or more of a slice, a streaming multiprocessor (SM), andthe processor.

Example 3 includes the subject matter of Examples 1-2, wherein thedetection/migration logic is further to migrate one or more threadsassociated with the one or more of the slice, the SM, and the processorfrom a first location to a second location, if the lack of balance isdetected.

Example 4 includes the subject matter of Examples 1-3, furthercomprising copy engine reference logic to copy thread context associatedwith one or more of the threads, wherein the copy reference logic isfurther to stream the thread context to the second location to preparefor migration of the one or more threads.

Example 5 includes the subject matter of Examples 1-4, furthercomprising communication/compatibility logic to synchronize the threadcontext copied at the second location with the one or more of the slice,the SM, and the processor to restart processing of the loads, whereinthe scheduling/loading logic to initiate load-balancing based on thesecond location.

Example 6 includes the subject matter of Examples 1-5, wherein thedetection/migration logic is further to facilitate migration of one ormore of the slice and the SM within the processor having multiple slicesor SMs in communication with one or more of hardware engine for threadstate migration, one or more local caches, one or more barriers, and ashared local memory (SLM), wherein the detection/migration logic isfurther to detect fragmentation in the SLM, wherein the fragmentationrefers to non-contiguous spaces within the SLM, and wherein thescheduling/loading logic is further to facilitate de-fragmentation ofthe SLM to create a contiguous space within the SLM.

Example 7 includes the subject matter of Examples 1-6, wherein theprocessor comprises a graphics processor that is co-located with anapplication processor on a common semiconductor package.

Some embodiments pertain to Example 8 that includes a method forfacilitating localized load-balancing for processors in computingdevices, the method comprising: hosting, at a processor, a localload-balancing mechanism; and serving as a local scheduler to maintainde-centralized load-balancing at the processor and between the processorand other one or more processors.

Example 9 includes the subject matter of Example 8, further comprisingdetecting lack of balance in distribution of the loads at the processor,wherein the lack of balance is due to one or more of a slice, astreaming multiprocessor (SM), and the processor.

Example 10 includes the subject matter of Examples 8-9, furthercomprising migrating one or more threads associated with the one or moreof the slice, the SM, and the processor from a first location to asecond location, if the lack of balance is detected.

Example 11 includes the subject matter of Examples 8-10, furthercomprising: copying thread context associated with one or more of thethreads; and streaming the thread context to the second location toprepare for migration of the one or more threads.

Example 12 includes the subject matter of Examples 8-11, furthercomprising: synchronizing the thread context copied at the secondlocation with the one or more of the slice, the SM, and the processor torestart processing of the loads; and initiating load-balancing based onthe second location.

Example 13 includes the subject matter of Examples 8-12, furthercomprising: facilitating migration of one or more of the slice and theSM within the processor having multiple slices or SMs in communicationwith one or more of hardware engine for thread state migration, one ormore local caches, one or more barriers, and a shared local memory(SLM); detecting fragmentation in the SLM, wherein the fragmentationrefers to non-contiguous spaces within the SLM; and facilitatingde-fragmentation of the SLM to create a contiguous space within the SLM.

Example 14 includes the subject matter of Examples 8-13, wherein theprocessor comprises a graphics processor that is co-located with anapplication processor on a common semiconductor package.

Some embodiments pertain to Example 15 that includes a graphicsprocessing system comprising a computing device having memory coupled toa processor, the processor to host a local load-balancing mechanism; andserve as a local scheduler to maintain de-centralized load-balancing atthe processor and between the processor and other one or moreprocessors.

Example 16 includes the subject matter of Example 15, wherein theprocessor is further to detect lack of balance in distribution of theloads at the processor, wherein the lack of balance is due to one ormore of a slice, a streaming multiprocessor (SM), and the processor.

Example 17 includes the subject matter of Example 15-16, wherein theprocessor is further to migrate one or more threads associated with theone or more of the slice, the SM, and the processor from a firstlocation to a second location, if the lack of balance is detected.

Example 18 includes the subject matter of Examples 15-17, wherein theprocessor is further to: copy thread context associated with one or moreof the threads; and stream the thread context to the second location toprepare for migration of the one or more threads.

Example 19 includes the subject matter of Example 15-18, wherein theprocessor is further to: synchronize the thread context copied at thesecond location with the one or more of the slice, the SM, and theprocessor to restart processing of the loads; and initiateload-balancing based on the second location.

Example 20 includes the subject matter of Example 15-19, wherein theprocessor is further to: facilitate migration of one or more of theslice and the SM within the processor having multiple slices or SMs incommunication with one or more of hardware engine for thread statemigration, one or more local caches, one or more barriers, and a sharedlocal memory (SLM); detect fragmentation in the SLM, wherein thefragmentation refers to non-contiguous spaces within the SLM; andfacilitate de-fragmentation of the SLM to create a contiguous spacewithin the SLM.

Example 21 includes the subject matter of Example 15-20, wherein theprocessor comprises a graphics processor that is co-located with anapplication processor on a common semiconductor package.

Example 29 includes at least one non-transitory or tangiblemachine-readable medium comprising a plurality of instructions, whenexecuted on a computing device, to implement or perform a method asclaimed in any of claims or examples 8-14.

Example 30 includes at least one machine-readable medium comprising aplurality of instructions, when executed on a computing device, toimplement or perform a method as claimed in any of claims or examples8-14.

Example 31 includes a system comprising a mechanism to implement orperform a method as claimed in any of claims or examples 8-14.

Example 32 includes an apparatus comprising means for performing amethod as claimed in any of claims or examples 8-14.

Example 33 includes a computing device arranged to implement or performa method as claimed in any of claims or examples 8-14.

Example 34 includes a communications device arranged to implement orperform a method as claimed in any of claims or examples 8-14.

Example 35 includes at least one machine-readable medium comprising aplurality of instructions, when executed on a computing device, toimplement or perform a method or realize an apparatus as claimed in anypreceding claims.

Example 36 includes at least one non-transitory or tangiblemachine-readable medium comprising a plurality of instructions, whenexecuted on a computing device, to implement or perform a method orrealize an apparatus as claimed in any preceding claims.

Example 37 includes a system comprising a mechanism to implement orperform a method or realize an apparatus as claimed in any precedingclaims.

Example 38 includes an apparatus comprising means to perform a method asclaimed in any preceding claims.

Example 39 includes a computing device arranged to implement or performa method or realize an apparatus as claimed in any preceding claims.

Example 40 includes a communications device arranged to implement orperform a method or realize an apparatus as claimed in any precedingclaims.

The drawings and the forgoing description give examples of embodiments.Those skilled in the art will appreciate that one or more of thedescribed elements may well be combined into a single functionalelement. Alternatively, certain elements may be split into multiplefunctional elements. Elements from one embodiment may be added toanother embodiment. For example, orders of processes described hereinmay be changed and are not limited to the manner described herein.Moreover, the actions of any flow diagram need not be implemented in theorder shown; nor do all of the acts necessarily need to be performed.Also, those acts that are not dependent on other acts may be performedin parallel with the other acts. The scope of embodiments is by no meanslimited by these specific examples. Numerous variations, whetherexplicitly given in the specification or not, such as differences instructure, dimension, and use of material, are possible. The scope ofembodiments is at least as broad as given by the following claims.

What is claimed is:
 1. An apparatus comprising: a plurality ofprocessors including at least a first graphics processor; and a locallink between the first graphics processor and one or more otherprocessors of the plurality of processors, wherein the first graphicsprocessor is to provide local load-balancing including: monitoring atthe first graphics processor to detect computing utilization in thegraphics processor and the one or more other processors; and performinglocal scheduling to maintain load-balancing within the first graphicsprocessor and between the first graphics processor and the one or moreother processors based at least in part on the detected computeutilization, wherein local scheduling includes, upon detecting animbalance in compute utilization, facilitating migration of one or morethreads from a first location to a second location within the firstgraphics processor and the one or more other processors, whereinfacilitating migration of the one or more threads includes the firstgraphics processor to: copy thread context associated with the one ormore threads, and stream the thread context to the second location toprepare for migration of the one or more threads.
 2. The apparatus ofclaim 1, wherein including the first graphics processor to: communicatecompute utilization information with the one or more other processorsvia the local link between the first graphics processor and the one ormore other processors.
 3. The apparatus of claim 1, wherein the firstlocation and the second location are within the first graphicsprocessor.
 4. The apparatus of claim 1, wherein the first location iswithin the first graphics processor and the second location is within aprocessor of the one or more other processors.
 5. The apparatus of claim1, wherein the first location is within a first slice and the secondlocation is within a second slice.
 6. The apparatus of claim 1, whereinfacilitating migration of the one or more threads further includes thefirst graphics processor to: synchronize the thread context copied atthe second location to restart processing of loads, and the firstgraphics processor to initiate load-balancing based on the secondlocation.
 7. The apparatus of claim 1, wherein the first graphicsprocessor is co-located with an application processor on a commonsemiconductor package.
 8. A method comprising: monitoring at a graphicsprocessor to detect computing utilization in the graphics processor andone or more other processors, a local link existing between the graphicsprocessor and the one or more other processors; and performing localscheduling to maintain load-balancing within the graphics processor andbetween the graphics processor and the one or more other processorsbased at least in part on the detected compute utilization; whereinlocal scheduling includes, upon detecting an imbalance in computeutilization, facilitating migration of one or more threads from a firstlocation to a second location within the graphics processor and the oneor more other processors, wherein facilitating migration of the one ormore threads includes: copying thread context associated with the one ormore threads, and streaming the thread context to the second location toprepare for migration of the one or more threads.
 9. The method of claim8, further comprising: communicating compute utilization informationbetween the graphics processor and the one or more other processors viathe local link between the graphics processor and the one or more otherprocessors.
 10. The method of claim 8, wherein the first location andthe second location are within the graphics processor.
 11. The method ofclaim 8, wherein the first location is within the graphics processor andthe second location is within a processor of the one or more otherprocessors.
 12. The method of claim 8, wherein facilitating migration ofthe one or more threads further includes: synchronizing the threadcontext copied at the second location to restart processing of loads;and initiating load-balancing based on the second location.
 13. At leastone non-transitory machine-readable medium comprising instructions thatwhen executed by a computing device, cause the computing device toperform operations comprising: monitoring at a graphics processor todetect computing utilization in the graphics processor and one or moreother processors, a local link existing between the graphics processorand the one or more other processors; and performing local scheduling tomaintain load-balancing at the graphics processor and between thegraphics processor and the one or more other processors based at leastin part on the detected compute utilization; wherein local schedulingincludes, upon detecting an imbalance in compute utilization,facilitating migration of one or more threads from a first location to asecond location within the graphics processor and the one or more otherprocessors, wherein facilitating migration of the one or more threadsincludes: copying thread context associated with the one or morethreads, and streaming the thread context to the second location toprepare for migration of the one or more threads.
 14. The non-transitorymachine-readable medium of claim 13, wherein the operations furthercomprise: communicating compute utilization information between thegraphics processor and the one or more other processors via the locallink between the graphics processor and the one or more otherprocessors.
 15. The non-transitory machine-readable medium of claim 13,wherein the first location and the second location are within thegraphics processor.
 16. The non-transitory machine-readable medium ofclaim 13, wherein the first location is within the graphics processorand the second location is within a processor of the one or more otherprocessors.
 17. The non-transitory machine-readable medium of claim 13,wherein facilitating migration of the one or more threads furtherincludes: synchronizing the thread context copied at the second locationto restart processing of loads; and initiating load-balancing based onthe second location.